[2023-03-21 12:07:47,963][23264] Saving configuration to /home/antpc/Desktop/rl_course/train_dir/default_experiment/config.json... [2023-03-21 12:07:47,963][23264] Rollout worker 0 uses device cpu [2023-03-21 12:07:47,963][23264] Rollout worker 1 uses device cpu [2023-03-21 12:07:47,964][23264] Rollout worker 2 uses device cpu [2023-03-21 12:07:47,964][23264] Rollout worker 3 uses device cpu [2023-03-21 12:07:47,964][23264] Rollout worker 4 uses device cpu [2023-03-21 12:07:47,964][23264] Rollout worker 5 uses device cpu [2023-03-21 12:07:47,964][23264] Rollout worker 6 uses device cpu [2023-03-21 12:07:47,964][23264] Rollout worker 7 uses device cpu [2023-03-21 12:07:48,005][23264] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-03-21 12:07:48,005][23264] InferenceWorker_p0-w0: min num requests: 2 [2023-03-21 12:07:48,020][23264] Starting all processes... [2023-03-21 12:07:48,020][23264] Starting process learner_proc0 [2023-03-21 12:07:48,688][23264] Starting all processes... [2023-03-21 12:07:48,691][23264] Starting process inference_proc0-0 [2023-03-21 12:07:48,691][23264] Starting process rollout_proc0 [2023-03-21 12:07:48,691][23332] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-03-21 12:07:48,692][23332] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 [2023-03-21 12:07:48,691][23264] Starting process rollout_proc1 [2023-03-21 12:07:48,700][23332] Num visible devices: 1 [2023-03-21 12:07:48,693][23264] Starting process rollout_proc2 [2023-03-21 12:07:48,694][23264] Starting process rollout_proc3 [2023-03-21 12:07:48,694][23264] Starting process rollout_proc4 [2023-03-21 12:07:48,696][23264] Starting process rollout_proc5 [2023-03-21 12:07:48,700][23264] Starting process rollout_proc6 [2023-03-21 12:07:48,701][23264] Starting process rollout_proc7 [2023-03-21 12:07:48,744][23332] Starting seed is not provided [2023-03-21 12:07:48,745][23332] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-03-21 12:07:48,745][23332] Initializing actor-critic model on device cuda:0 [2023-03-21 12:07:48,745][23332] RunningMeanStd input shape: (3, 72, 128) [2023-03-21 12:07:48,746][23332] RunningMeanStd input shape: (1,) [2023-03-21 12:07:48,758][23332] ConvEncoder: input_channels=3 [2023-03-21 12:07:48,873][23332] Conv encoder output size: 512 [2023-03-21 12:07:48,873][23332] Policy head output size: 512 [2023-03-21 12:07:48,886][23332] Created Actor Critic model with architecture: [2023-03-21 12:07:48,886][23332] ActorCriticSharedWeights( (obs_normalizer): ObservationNormalizer( (running_mean_std): RunningMeanStdDictInPlace( (running_mean_std): ModuleDict( (obs): RunningMeanStdInPlace() ) ) ) (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace) (encoder): VizdoomEncoder( (basic_encoder): ConvEncoder( (enc): RecursiveScriptModule( original_name=ConvEncoderImpl (conv_head): RecursiveScriptModule( original_name=Sequential (0): RecursiveScriptModule(original_name=Conv2d) (1): RecursiveScriptModule(original_name=ELU) (2): RecursiveScriptModule(original_name=Conv2d) (3): RecursiveScriptModule(original_name=ELU) (4): RecursiveScriptModule(original_name=Conv2d) (5): RecursiveScriptModule(original_name=ELU) ) (mlp_layers): RecursiveScriptModule( original_name=Sequential (0): RecursiveScriptModule(original_name=Linear) (1): RecursiveScriptModule(original_name=ELU) ) ) ) ) (core): ModelCoreRNN( (core): GRU(512, 512) ) (decoder): MlpDecoder( (mlp): Identity() ) (critic_linear): Linear(in_features=512, out_features=1, bias=True) (action_parameterization): ActionParameterizationDefault( (distribution_linear): Linear(in_features=512, out_features=5, bias=True) ) ) [2023-03-21 12:07:49,710][23363] Worker 1 uses CPU cores [2, 3] [2023-03-21 12:07:49,746][23366] Worker 4 uses CPU cores [8, 9] [2023-03-21 12:07:49,850][23365] Worker 3 uses CPU cores [6, 7] [2023-03-21 12:07:49,866][23367] Worker 5 uses CPU cores [10, 11] [2023-03-21 12:07:49,894][23383] Worker 6 uses CPU cores [12, 13] [2023-03-21 12:07:49,902][23361] Worker 0 uses CPU cores [0, 1] [2023-03-21 12:07:49,913][23384] Worker 7 uses CPU cores [14, 15] [2023-03-21 12:07:49,930][23364] Worker 2 uses CPU cores [4, 5] [2023-03-21 12:07:49,995][23362] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-03-21 12:07:49,995][23362] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 [2023-03-21 12:07:50,004][23362] Num visible devices: 1 [2023-03-21 12:07:50,512][23332] Using optimizer [2023-03-21 12:07:50,512][23332] No checkpoints found [2023-03-21 12:07:50,512][23332] Did not load from checkpoint, starting from scratch! [2023-03-21 12:07:50,512][23332] Initialized policy 0 weights for model version 0 [2023-03-21 12:07:50,513][23332] LearnerWorker_p0 finished initialization! [2023-03-21 12:07:50,514][23332] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-03-21 12:07:50,560][23362] RunningMeanStd input shape: (3, 72, 128) [2023-03-21 12:07:50,560][23362] RunningMeanStd input shape: (1,) [2023-03-21 12:07:50,568][23362] ConvEncoder: input_channels=3 [2023-03-21 12:07:50,623][23362] Conv encoder output size: 512 [2023-03-21 12:07:50,623][23362] Policy head output size: 512 [2023-03-21 12:07:51,490][23264] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) [2023-03-21 12:07:51,846][23264] Inference worker 0-0 is ready! [2023-03-21 12:07:51,846][23264] All inference workers are ready! Signal rollout workers to start! [2023-03-21 12:07:51,861][23361] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:51,862][23366] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:51,863][23384] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:51,863][23367] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:51,864][23364] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:51,864][23365] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:51,864][23383] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:51,864][23363] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:07:52,025][23364] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,026][23384] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,059][23361] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,060][23363] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,061][23367] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,078][23366] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,175][23384] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,225][23367] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,226][23363] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,234][23364] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,236][23365] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,239][23361] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,268][23366] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,374][23365] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,386][23363] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,418][23364] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,427][23366] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,445][23383] Decorrelating experience for 0 frames... [2023-03-21 12:07:52,553][23361] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,558][23363] Decorrelating experience for 96 frames... [2023-03-21 12:07:52,565][23365] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,618][23364] Decorrelating experience for 96 frames... [2023-03-21 12:07:52,632][23383] Decorrelating experience for 32 frames... [2023-03-21 12:07:52,652][23384] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,736][23367] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,748][23361] Decorrelating experience for 96 frames... [2023-03-21 12:07:52,752][23365] Decorrelating experience for 96 frames... [2023-03-21 12:07:52,793][23366] Decorrelating experience for 96 frames... [2023-03-21 12:07:52,824][23383] Decorrelating experience for 64 frames... [2023-03-21 12:07:52,843][23384] Decorrelating experience for 96 frames... [2023-03-21 12:07:52,893][23367] Decorrelating experience for 96 frames... [2023-03-21 12:07:52,981][23383] Decorrelating experience for 96 frames... [2023-03-21 12:07:53,334][23332] Signal inference workers to stop experience collection... [2023-03-21 12:07:53,337][23362] InferenceWorker_p0-w0: stopping experience collection [2023-03-21 12:07:54,059][23332] Signal inference workers to resume experience collection... [2023-03-21 12:07:54,060][23362] InferenceWorker_p0-w0: resuming experience collection [2023-03-21 12:07:55,468][23362] Updated weights for policy 0, policy_version 10 (0.0185) [2023-03-21 12:07:56,490][23264] Fps is (10 sec: 13926.6, 60 sec: 13926.6, 300 sec: 13926.6). Total num frames: 69632. Throughput: 0: 1115.2. Samples: 5576. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) [2023-03-21 12:07:56,490][23264] Avg episode reward: [(0, '4.399')] [2023-03-21 12:07:56,754][23362] Updated weights for policy 0, policy_version 20 (0.0007) [2023-03-21 12:07:58,045][23362] Updated weights for policy 0, policy_version 30 (0.0006) [2023-03-21 12:07:59,348][23362] Updated weights for policy 0, policy_version 40 (0.0006) [2023-03-21 12:08:00,646][23362] Updated weights for policy 0, policy_version 50 (0.0006) [2023-03-21 12:08:01,490][23264] Fps is (10 sec: 22937.7, 60 sec: 22937.7, 300 sec: 22937.7). Total num frames: 229376. Throughput: 0: 5343.4. Samples: 53434. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:08:01,490][23264] Avg episode reward: [(0, '4.422')] [2023-03-21 12:08:01,490][23332] Saving new best policy, reward=4.422! [2023-03-21 12:08:01,931][23362] Updated weights for policy 0, policy_version 60 (0.0006) [2023-03-21 12:08:03,249][23362] Updated weights for policy 0, policy_version 70 (0.0007) [2023-03-21 12:08:04,554][23362] Updated weights for policy 0, policy_version 80 (0.0006) [2023-03-21 12:08:05,847][23362] Updated weights for policy 0, policy_version 90 (0.0006) [2023-03-21 12:08:06,490][23264] Fps is (10 sec: 31539.2, 60 sec: 25668.4, 300 sec: 25668.4). Total num frames: 385024. Throughput: 0: 5141.4. Samples: 77120. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-03-21 12:08:06,490][23264] Avg episode reward: [(0, '4.528')] [2023-03-21 12:08:06,498][23332] Saving new best policy, reward=4.512! [2023-03-21 12:08:07,191][23362] Updated weights for policy 0, policy_version 100 (0.0007) [2023-03-21 12:08:08,002][23264] Heartbeat connected on Batcher_0 [2023-03-21 12:08:08,003][23264] Heartbeat connected on LearnerWorker_p0 [2023-03-21 12:08:08,008][23264] Heartbeat connected on InferenceWorker_p0-w0 [2023-03-21 12:08:08,009][23264] Heartbeat connected on RolloutWorker_w0 [2023-03-21 12:08:08,011][23264] Heartbeat connected on RolloutWorker_w1 [2023-03-21 12:08:08,012][23264] Heartbeat connected on RolloutWorker_w2 [2023-03-21 12:08:08,013][23264] Heartbeat connected on RolloutWorker_w3 [2023-03-21 12:08:08,015][23264] Heartbeat connected on RolloutWorker_w4 [2023-03-21 12:08:08,017][23264] Heartbeat connected on RolloutWorker_w5 [2023-03-21 12:08:08,018][23264] Heartbeat connected on RolloutWorker_w6 [2023-03-21 12:08:08,022][23264] Heartbeat connected on RolloutWorker_w7 [2023-03-21 12:08:08,497][23362] Updated weights for policy 0, policy_version 110 (0.0007) [2023-03-21 12:08:09,815][23362] Updated weights for policy 0, policy_version 120 (0.0006) [2023-03-21 12:08:11,084][23362] Updated weights for policy 0, policy_version 130 (0.0006) [2023-03-21 12:08:11,490][23264] Fps is (10 sec: 31539.2, 60 sec: 27238.5, 300 sec: 27238.5). Total num frames: 544768. Throughput: 0: 6191.7. Samples: 123834. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:08:11,490][23264] Avg episode reward: [(0, '4.622')] [2023-03-21 12:08:11,490][23332] Saving new best policy, reward=4.622! [2023-03-21 12:08:12,437][23362] Updated weights for policy 0, policy_version 140 (0.0007) [2023-03-21 12:08:13,745][23362] Updated weights for policy 0, policy_version 150 (0.0006) [2023-03-21 12:08:15,051][23362] Updated weights for policy 0, policy_version 160 (0.0007) [2023-03-21 12:08:16,339][23362] Updated weights for policy 0, policy_version 170 (0.0006) [2023-03-21 12:08:16,490][23264] Fps is (10 sec: 31539.0, 60 sec: 28016.6, 300 sec: 28016.6). Total num frames: 700416. Throughput: 0: 6834.1. Samples: 170852. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2023-03-21 12:08:16,490][23264] Avg episode reward: [(0, '4.658')] [2023-03-21 12:08:16,492][23332] Saving new best policy, reward=4.658! [2023-03-21 12:08:17,691][23362] Updated weights for policy 0, policy_version 180 (0.0007) [2023-03-21 12:08:19,005][23362] Updated weights for policy 0, policy_version 190 (0.0006) [2023-03-21 12:08:20,340][23362] Updated weights for policy 0, policy_version 200 (0.0007) [2023-03-21 12:08:21,490][23264] Fps is (10 sec: 30719.9, 60 sec: 28398.9, 300 sec: 28398.9). Total num frames: 851968. Throughput: 0: 6470.0. Samples: 194100. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-03-21 12:08:21,490][23264] Avg episode reward: [(0, '4.958')] [2023-03-21 12:08:21,497][23332] Saving new best policy, reward=4.958! [2023-03-21 12:08:21,635][23362] Updated weights for policy 0, policy_version 210 (0.0006) [2023-03-21 12:08:23,015][23362] Updated weights for policy 0, policy_version 220 (0.0007) [2023-03-21 12:08:24,362][23362] Updated weights for policy 0, policy_version 230 (0.0007) [2023-03-21 12:08:25,704][23362] Updated weights for policy 0, policy_version 240 (0.0007) [2023-03-21 12:08:26,490][23264] Fps is (10 sec: 30310.4, 60 sec: 28672.0, 300 sec: 28672.0). Total num frames: 1003520. Throughput: 0: 6855.1. Samples: 239928. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-03-21 12:08:26,490][23264] Avg episode reward: [(0, '5.006')] [2023-03-21 12:08:26,517][23332] Saving new best policy, reward=5.006! [2023-03-21 12:08:27,127][23362] Updated weights for policy 0, policy_version 250 (0.0007) [2023-03-21 12:08:28,514][23362] Updated weights for policy 0, policy_version 260 (0.0007) [2023-03-21 12:08:29,862][23362] Updated weights for policy 0, policy_version 270 (0.0006) [2023-03-21 12:08:31,188][23362] Updated weights for policy 0, policy_version 280 (0.0006) [2023-03-21 12:08:31,489][23264] Fps is (10 sec: 30310.6, 60 sec: 28876.9, 300 sec: 28876.9). Total num frames: 1155072. Throughput: 0: 7117.0. Samples: 284678. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:08:31,490][23264] Avg episode reward: [(0, '5.682')] [2023-03-21 12:08:31,490][23332] Saving new best policy, reward=5.682! [2023-03-21 12:08:32,551][23362] Updated weights for policy 0, policy_version 290 (0.0007) [2023-03-21 12:08:33,987][23362] Updated weights for policy 0, policy_version 300 (0.0007) [2023-03-21 12:08:35,414][23362] Updated weights for policy 0, policy_version 310 (0.0007) [2023-03-21 12:08:36,489][23264] Fps is (10 sec: 29901.0, 60 sec: 28945.1, 300 sec: 28945.1). Total num frames: 1302528. Throughput: 0: 6817.4. Samples: 306784. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-03-21 12:08:36,490][23264] Avg episode reward: [(0, '7.299')] [2023-03-21 12:08:36,493][23332] Saving new best policy, reward=7.299! [2023-03-21 12:08:36,776][23362] Updated weights for policy 0, policy_version 320 (0.0007) [2023-03-21 12:08:38,126][23362] Updated weights for policy 0, policy_version 330 (0.0006) [2023-03-21 12:08:39,441][23362] Updated weights for policy 0, policy_version 340 (0.0006) [2023-03-21 12:08:40,735][23362] Updated weights for policy 0, policy_version 350 (0.0006) [2023-03-21 12:08:41,490][23264] Fps is (10 sec: 29900.7, 60 sec: 29081.6, 300 sec: 29081.6). Total num frames: 1454080. Throughput: 0: 7699.8. Samples: 352068. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2023-03-21 12:08:41,490][23264] Avg episode reward: [(0, '8.136')] [2023-03-21 12:08:41,490][23332] Saving new best policy, reward=8.136! [2023-03-21 12:08:42,130][23362] Updated weights for policy 0, policy_version 360 (0.0007) [2023-03-21 12:08:43,501][23362] Updated weights for policy 0, policy_version 370 (0.0007) [2023-03-21 12:08:44,867][23362] Updated weights for policy 0, policy_version 380 (0.0007) [2023-03-21 12:08:46,180][23362] Updated weights for policy 0, policy_version 390 (0.0007) [2023-03-21 12:08:46,490][23264] Fps is (10 sec: 30310.3, 60 sec: 29193.3, 300 sec: 29193.3). Total num frames: 1605632. Throughput: 0: 7648.2. Samples: 397602. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:08:46,490][23264] Avg episode reward: [(0, '10.632')] [2023-03-21 12:08:46,492][23332] Saving new best policy, reward=10.632! [2023-03-21 12:08:47,586][23362] Updated weights for policy 0, policy_version 400 (0.0007) [2023-03-21 12:08:48,950][23362] Updated weights for policy 0, policy_version 410 (0.0007) [2023-03-21 12:08:50,286][23362] Updated weights for policy 0, policy_version 420 (0.0006) [2023-03-21 12:08:51,490][23264] Fps is (10 sec: 29900.8, 60 sec: 29218.2, 300 sec: 29218.2). Total num frames: 1753088. Throughput: 0: 7617.7. Samples: 419918. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:08:51,490][23264] Avg episode reward: [(0, '13.298')] [2023-03-21 12:08:51,497][23332] Saving new best policy, reward=13.298! [2023-03-21 12:08:51,645][23362] Updated weights for policy 0, policy_version 430 (0.0007) [2023-03-21 12:08:53,016][23362] Updated weights for policy 0, policy_version 440 (0.0007) [2023-03-21 12:08:54,416][23362] Updated weights for policy 0, policy_version 450 (0.0007) [2023-03-21 12:08:55,791][23362] Updated weights for policy 0, policy_version 460 (0.0007) [2023-03-21 12:08:56,490][23264] Fps is (10 sec: 29900.7, 60 sec: 30583.4, 300 sec: 29302.2). Total num frames: 1904640. Throughput: 0: 7575.3. Samples: 464724. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-03-21 12:08:56,490][23264] Avg episode reward: [(0, '16.158')] [2023-03-21 12:08:56,493][23332] Saving new best policy, reward=16.158! [2023-03-21 12:08:57,177][23362] Updated weights for policy 0, policy_version 470 (0.0006) [2023-03-21 12:08:58,562][23362] Updated weights for policy 0, policy_version 480 (0.0007) [2023-03-21 12:08:59,926][23362] Updated weights for policy 0, policy_version 490 (0.0006) [2023-03-21 12:09:01,309][23362] Updated weights for policy 0, policy_version 500 (0.0007) [2023-03-21 12:09:01,490][23264] Fps is (10 sec: 29900.8, 60 sec: 30378.7, 300 sec: 29315.7). Total num frames: 2052096. Throughput: 0: 7526.1. Samples: 509524. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:09:01,490][23264] Avg episode reward: [(0, '15.498')] [2023-03-21 12:09:02,633][23362] Updated weights for policy 0, policy_version 510 (0.0007) [2023-03-21 12:09:03,969][23362] Updated weights for policy 0, policy_version 520 (0.0007) [2023-03-21 12:09:05,326][23362] Updated weights for policy 0, policy_version 530 (0.0007) [2023-03-21 12:09:06,489][23264] Fps is (10 sec: 29901.0, 60 sec: 30310.4, 300 sec: 29382.0). Total num frames: 2203648. Throughput: 0: 7516.2. Samples: 532328. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:09:06,490][23264] Avg episode reward: [(0, '16.303')] [2023-03-21 12:09:06,492][23332] Saving new best policy, reward=16.303! [2023-03-21 12:09:06,716][23362] Updated weights for policy 0, policy_version 540 (0.0007) [2023-03-21 12:09:08,130][23362] Updated weights for policy 0, policy_version 550 (0.0007) [2023-03-21 12:09:09,517][23362] Updated weights for policy 0, policy_version 560 (0.0006) [2023-03-21 12:09:10,886][23362] Updated weights for policy 0, policy_version 570 (0.0007) [2023-03-21 12:09:11,489][23264] Fps is (10 sec: 29900.9, 60 sec: 30105.6, 300 sec: 29388.8). Total num frames: 2351104. Throughput: 0: 7483.2. Samples: 576670. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:09:11,490][23264] Avg episode reward: [(0, '20.481')] [2023-03-21 12:09:11,490][23332] Saving new best policy, reward=20.481! [2023-03-21 12:09:12,217][23362] Updated weights for policy 0, policy_version 580 (0.0007) [2023-03-21 12:09:13,531][23362] Updated weights for policy 0, policy_version 590 (0.0006) [2023-03-21 12:09:14,917][23362] Updated weights for policy 0, policy_version 600 (0.0007) [2023-03-21 12:09:16,298][23362] Updated weights for policy 0, policy_version 610 (0.0007) [2023-03-21 12:09:16,490][23264] Fps is (10 sec: 29900.6, 60 sec: 30037.3, 300 sec: 29443.0). Total num frames: 2502656. Throughput: 0: 7496.2. Samples: 622008. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:09:16,490][23264] Avg episode reward: [(0, '21.688')] [2023-03-21 12:09:16,492][23332] Saving new best policy, reward=21.688! [2023-03-21 12:09:17,718][23362] Updated weights for policy 0, policy_version 620 (0.0007) [2023-03-21 12:09:19,104][23362] Updated weights for policy 0, policy_version 630 (0.0007) [2023-03-21 12:09:20,476][23362] Updated weights for policy 0, policy_version 640 (0.0007) [2023-03-21 12:09:21,490][23264] Fps is (10 sec: 29900.6, 60 sec: 29969.1, 300 sec: 29445.7). Total num frames: 2650112. Throughput: 0: 7490.1. Samples: 643840. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:09:21,490][23264] Avg episode reward: [(0, '20.951')] [2023-03-21 12:09:21,846][23362] Updated weights for policy 0, policy_version 650 (0.0007) [2023-03-21 12:09:23,224][23362] Updated weights for policy 0, policy_version 660 (0.0007) [2023-03-21 12:09:24,501][23362] Updated weights for policy 0, policy_version 670 (0.0006) [2023-03-21 12:09:25,824][23362] Updated weights for policy 0, policy_version 680 (0.0006) [2023-03-21 12:09:26,490][23264] Fps is (10 sec: 30310.5, 60 sec: 30037.4, 300 sec: 29534.3). Total num frames: 2805760. Throughput: 0: 7497.3. Samples: 689446. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:09:26,490][23264] Avg episode reward: [(0, '21.432')] [2023-03-21 12:09:27,129][23362] Updated weights for policy 0, policy_version 690 (0.0006) [2023-03-21 12:09:28,442][23362] Updated weights for policy 0, policy_version 700 (0.0007) [2023-03-21 12:09:29,768][23362] Updated weights for policy 0, policy_version 710 (0.0006) [2023-03-21 12:09:31,046][23362] Updated weights for policy 0, policy_version 720 (0.0006) [2023-03-21 12:09:31,490][23264] Fps is (10 sec: 31129.6, 60 sec: 30105.6, 300 sec: 29614.1). Total num frames: 2961408. Throughput: 0: 7532.7. Samples: 736574. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:09:31,490][23264] Avg episode reward: [(0, '21.771')] [2023-03-21 12:09:31,490][23332] Saving new best policy, reward=21.771! [2023-03-21 12:09:32,388][23362] Updated weights for policy 0, policy_version 730 (0.0006) [2023-03-21 12:09:33,740][23362] Updated weights for policy 0, policy_version 740 (0.0007) [2023-03-21 12:09:35,062][23362] Updated weights for policy 0, policy_version 750 (0.0006) [2023-03-21 12:09:36,347][23362] Updated weights for policy 0, policy_version 760 (0.0007) [2023-03-21 12:09:36,490][23264] Fps is (10 sec: 31129.6, 60 sec: 30242.1, 300 sec: 29686.3). Total num frames: 3117056. Throughput: 0: 7546.8. Samples: 759524. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:09:36,490][23264] Avg episode reward: [(0, '26.696')] [2023-03-21 12:09:36,493][23332] Saving new best policy, reward=26.696! [2023-03-21 12:09:37,723][23362] Updated weights for policy 0, policy_version 770 (0.0007) [2023-03-21 12:09:39,101][23362] Updated weights for policy 0, policy_version 780 (0.0007) [2023-03-21 12:09:40,460][23362] Updated weights for policy 0, policy_version 790 (0.0006) [2023-03-21 12:09:41,490][23264] Fps is (10 sec: 30310.4, 60 sec: 30173.9, 300 sec: 29677.4). Total num frames: 3264512. Throughput: 0: 7563.3. Samples: 805072. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:09:41,490][23264] Avg episode reward: [(0, '25.249')] [2023-03-21 12:09:41,786][23362] Updated weights for policy 0, policy_version 800 (0.0006) [2023-03-21 12:09:43,106][23362] Updated weights for policy 0, policy_version 810 (0.0006) [2023-03-21 12:09:44,435][23362] Updated weights for policy 0, policy_version 820 (0.0006) [2023-03-21 12:09:45,761][23362] Updated weights for policy 0, policy_version 830 (0.0006) [2023-03-21 12:09:46,490][23264] Fps is (10 sec: 30310.2, 60 sec: 30242.1, 300 sec: 29740.5). Total num frames: 3420160. Throughput: 0: 7593.7. Samples: 851242. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2023-03-21 12:09:46,490][23264] Avg episode reward: [(0, '24.136')] [2023-03-21 12:09:46,493][23332] Saving /home/antpc/Desktop/rl_course/train_dir/default_experiment/checkpoint_p0/checkpoint_000000835_3420160.pth... [2023-03-21 12:09:47,117][23362] Updated weights for policy 0, policy_version 840 (0.0007) [2023-03-21 12:09:48,478][23362] Updated weights for policy 0, policy_version 850 (0.0006) [2023-03-21 12:09:49,785][23362] Updated weights for policy 0, policy_version 860 (0.0007) [2023-03-21 12:09:51,117][23362] Updated weights for policy 0, policy_version 870 (0.0006) [2023-03-21 12:09:51,490][23264] Fps is (10 sec: 30720.1, 60 sec: 30310.4, 300 sec: 29764.3). Total num frames: 3571712. Throughput: 0: 7596.4. Samples: 874168. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:09:51,490][23264] Avg episode reward: [(0, '23.190')] [2023-03-21 12:09:52,475][23362] Updated weights for policy 0, policy_version 880 (0.0006) [2023-03-21 12:09:54,011][23362] Updated weights for policy 0, policy_version 890 (0.0007) [2023-03-21 12:09:55,605][23362] Updated weights for policy 0, policy_version 900 (0.0007) [2023-03-21 12:09:56,490][23264] Fps is (10 sec: 29081.7, 60 sec: 30105.6, 300 sec: 29687.8). Total num frames: 3710976. Throughput: 0: 7569.7. Samples: 917306. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0) [2023-03-21 12:09:56,490][23264] Avg episode reward: [(0, '23.791')] [2023-03-21 12:09:57,046][23362] Updated weights for policy 0, policy_version 910 (0.0007) [2023-03-21 12:09:58,423][23362] Updated weights for policy 0, policy_version 920 (0.0006) [2023-03-21 12:09:59,784][23362] Updated weights for policy 0, policy_version 930 (0.0007) [2023-03-21 12:10:01,108][23362] Updated weights for policy 0, policy_version 940 (0.0006) [2023-03-21 12:10:01,490][23264] Fps is (10 sec: 28671.9, 60 sec: 30105.6, 300 sec: 29680.2). Total num frames: 3858432. Throughput: 0: 7539.2. Samples: 961274. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:10:01,490][23264] Avg episode reward: [(0, '24.487')] [2023-03-21 12:10:02,447][23362] Updated weights for policy 0, policy_version 950 (0.0006) [2023-03-21 12:10:03,820][23362] Updated weights for policy 0, policy_version 960 (0.0007) [2023-03-21 12:10:05,195][23362] Updated weights for policy 0, policy_version 970 (0.0007) [2023-03-21 12:10:06,490][23264] Fps is (10 sec: 29900.8, 60 sec: 30105.6, 300 sec: 29703.6). Total num frames: 4009984. Throughput: 0: 7559.0. Samples: 983994. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:10:06,490][23264] Avg episode reward: [(0, '24.296')] [2023-03-21 12:10:06,530][23362] Updated weights for policy 0, policy_version 980 (0.0006) [2023-03-21 12:10:07,839][23362] Updated weights for policy 0, policy_version 990 (0.0007) [2023-03-21 12:10:09,124][23362] Updated weights for policy 0, policy_version 1000 (0.0007) [2023-03-21 12:10:10,466][23362] Updated weights for policy 0, policy_version 1010 (0.0007) [2023-03-21 12:10:11,490][23264] Fps is (10 sec: 30720.1, 60 sec: 30242.1, 300 sec: 29754.5). Total num frames: 4165632. Throughput: 0: 7571.6. Samples: 1030168. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-03-21 12:10:11,490][23264] Avg episode reward: [(0, '26.925')] [2023-03-21 12:10:11,490][23332] Saving new best policy, reward=26.925! [2023-03-21 12:10:11,820][23362] Updated weights for policy 0, policy_version 1020 (0.0007) [2023-03-21 12:10:13,191][23362] Updated weights for policy 0, policy_version 1030 (0.0007) [2023-03-21 12:10:14,546][23362] Updated weights for policy 0, policy_version 1040 (0.0007) [2023-03-21 12:10:15,835][23362] Updated weights for policy 0, policy_version 1050 (0.0006) [2023-03-21 12:10:16,489][23264] Fps is (10 sec: 31129.9, 60 sec: 30310.5, 300 sec: 29802.0). Total num frames: 4321280. Throughput: 0: 7544.5. Samples: 1076076. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-03-21 12:10:16,490][23264] Avg episode reward: [(0, '28.370')] [2023-03-21 12:10:16,492][23332] Saving new best policy, reward=28.370! [2023-03-21 12:10:17,194][23362] Updated weights for policy 0, policy_version 1060 (0.0007) [2023-03-21 12:10:18,503][23362] Updated weights for policy 0, policy_version 1070 (0.0006) [2023-03-21 12:10:19,781][23362] Updated weights for policy 0, policy_version 1080 (0.0007) [2023-03-21 12:10:21,061][23362] Updated weights for policy 0, policy_version 1090 (0.0006) [2023-03-21 12:10:21,490][23264] Fps is (10 sec: 31129.6, 60 sec: 30446.9, 300 sec: 29846.2). Total num frames: 4476928. Throughput: 0: 7552.0. Samples: 1099362. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:10:21,490][23264] Avg episode reward: [(0, '26.373')] [2023-03-21 12:10:22,343][23362] Updated weights for policy 0, policy_version 1100 (0.0006) [2023-03-21 12:10:23,645][23362] Updated weights for policy 0, policy_version 1110 (0.0007) [2023-03-21 12:10:24,957][23362] Updated weights for policy 0, policy_version 1120 (0.0006) [2023-03-21 12:10:26,233][23362] Updated weights for policy 0, policy_version 1130 (0.0006) [2023-03-21 12:10:26,490][23264] Fps is (10 sec: 31538.9, 60 sec: 30515.2, 300 sec: 29914.0). Total num frames: 4636672. Throughput: 0: 7594.3. Samples: 1146816. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:10:26,490][23264] Avg episode reward: [(0, '24.247')] [2023-03-21 12:10:27,517][23362] Updated weights for policy 0, policy_version 1140 (0.0006) [2023-03-21 12:10:28,810][23362] Updated weights for policy 0, policy_version 1150 (0.0006) [2023-03-21 12:10:30,079][23362] Updated weights for policy 0, policy_version 1160 (0.0006) [2023-03-21 12:10:31,371][23362] Updated weights for policy 0, policy_version 1170 (0.0006) [2023-03-21 12:10:31,489][23264] Fps is (10 sec: 31539.3, 60 sec: 30515.2, 300 sec: 29952.0). Total num frames: 4792320. Throughput: 0: 7627.9. Samples: 1194496. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:10:31,490][23264] Avg episode reward: [(0, '24.420')] [2023-03-21 12:10:32,657][23362] Updated weights for policy 0, policy_version 1180 (0.0006) [2023-03-21 12:10:33,940][23362] Updated weights for policy 0, policy_version 1190 (0.0006) [2023-03-21 12:10:35,262][23362] Updated weights for policy 0, policy_version 1200 (0.0007) [2023-03-21 12:10:36,490][23264] Fps is (10 sec: 31539.1, 60 sec: 30583.5, 300 sec: 30012.5). Total num frames: 4952064. Throughput: 0: 7649.0. Samples: 1218372. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:10:36,490][23264] Avg episode reward: [(0, '26.496')] [2023-03-21 12:10:36,557][23362] Updated weights for policy 0, policy_version 1210 (0.0007) [2023-03-21 12:10:37,902][23362] Updated weights for policy 0, policy_version 1220 (0.0006) [2023-03-21 12:10:39,211][23362] Updated weights for policy 0, policy_version 1230 (0.0007) [2023-03-21 12:10:40,496][23362] Updated weights for policy 0, policy_version 1240 (0.0006) [2023-03-21 12:10:41,490][23264] Fps is (10 sec: 31539.1, 60 sec: 30720.0, 300 sec: 30045.4). Total num frames: 5107712. Throughput: 0: 7731.7. Samples: 1265232. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-03-21 12:10:41,490][23264] Avg episode reward: [(0, '25.147')] [2023-03-21 12:10:41,782][23362] Updated weights for policy 0, policy_version 1250 (0.0006) [2023-03-21 12:10:43,055][23362] Updated weights for policy 0, policy_version 1260 (0.0006) [2023-03-21 12:10:44,340][23362] Updated weights for policy 0, policy_version 1270 (0.0006) [2023-03-21 12:10:45,602][23362] Updated weights for policy 0, policy_version 1280 (0.0006) [2023-03-21 12:10:46,490][23264] Fps is (10 sec: 31539.3, 60 sec: 30788.3, 300 sec: 30099.8). Total num frames: 5267456. Throughput: 0: 7824.9. Samples: 1313396. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-03-21 12:10:46,490][23264] Avg episode reward: [(0, '28.868')] [2023-03-21 12:10:46,501][23332] Saving new best policy, reward=28.868! [2023-03-21 12:10:46,944][23362] Updated weights for policy 0, policy_version 1290 (0.0007) [2023-03-21 12:10:48,278][23362] Updated weights for policy 0, policy_version 1300 (0.0007) [2023-03-21 12:10:49,594][23362] Updated weights for policy 0, policy_version 1310 (0.0007) [2023-03-21 12:10:50,928][23362] Updated weights for policy 0, policy_version 1320 (0.0007) [2023-03-21 12:10:51,490][23264] Fps is (10 sec: 31539.1, 60 sec: 30856.5, 300 sec: 30128.4). Total num frames: 5423104. Throughput: 0: 7828.4. Samples: 1336274. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-03-21 12:10:51,490][23264] Avg episode reward: [(0, '25.935')] [2023-03-21 12:10:52,219][23362] Updated weights for policy 0, policy_version 1330 (0.0006) [2023-03-21 12:10:53,559][23362] Updated weights for policy 0, policy_version 1340 (0.0006) [2023-03-21 12:10:54,841][23362] Updated weights for policy 0, policy_version 1350 (0.0006) [2023-03-21 12:10:56,151][23362] Updated weights for policy 0, policy_version 1360 (0.0007) [2023-03-21 12:10:56,490][23264] Fps is (10 sec: 31129.5, 60 sec: 31129.6, 300 sec: 30155.4). Total num frames: 5578752. Throughput: 0: 7843.5. Samples: 1383124. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:10:56,490][23264] Avg episode reward: [(0, '26.315')] [2023-03-21 12:10:57,481][23362] Updated weights for policy 0, policy_version 1370 (0.0006) [2023-03-21 12:10:58,821][23362] Updated weights for policy 0, policy_version 1380 (0.0006) [2023-03-21 12:11:00,134][23362] Updated weights for policy 0, policy_version 1390 (0.0006) [2023-03-21 12:11:01,399][23362] Updated weights for policy 0, policy_version 1400 (0.0006) [2023-03-21 12:11:01,490][23264] Fps is (10 sec: 31129.6, 60 sec: 31266.2, 300 sec: 30181.1). Total num frames: 5734400. Throughput: 0: 7861.1. Samples: 1429826. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:11:01,490][23264] Avg episode reward: [(0, '27.340')] [2023-03-21 12:11:02,685][23362] Updated weights for policy 0, policy_version 1410 (0.0007) [2023-03-21 12:11:03,988][23362] Updated weights for policy 0, policy_version 1420 (0.0006) [2023-03-21 12:11:05,293][23362] Updated weights for policy 0, policy_version 1430 (0.0007) [2023-03-21 12:11:06,490][23264] Fps is (10 sec: 31129.7, 60 sec: 31334.4, 300 sec: 30205.4). Total num frames: 5890048. Throughput: 0: 7873.6. Samples: 1453674. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-03-21 12:11:06,490][23264] Avg episode reward: [(0, '26.217')] [2023-03-21 12:11:06,648][23362] Updated weights for policy 0, policy_version 1440 (0.0007) [2023-03-21 12:11:07,985][23362] Updated weights for policy 0, policy_version 1450 (0.0007) [2023-03-21 12:11:09,272][23362] Updated weights for policy 0, policy_version 1460 (0.0007) [2023-03-21 12:11:10,585][23362] Updated weights for policy 0, policy_version 1470 (0.0006) [2023-03-21 12:11:11,489][23264] Fps is (10 sec: 31129.7, 60 sec: 31334.4, 300 sec: 30228.5). Total num frames: 6045696. Throughput: 0: 7852.1. Samples: 1500162. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2023-03-21 12:11:11,490][23264] Avg episode reward: [(0, '24.863')] [2023-03-21 12:11:11,888][23362] Updated weights for policy 0, policy_version 1480 (0.0006) [2023-03-21 12:11:13,181][23362] Updated weights for policy 0, policy_version 1490 (0.0006) [2023-03-21 12:11:14,525][23362] Updated weights for policy 0, policy_version 1500 (0.0006) [2023-03-21 12:11:15,838][23362] Updated weights for policy 0, policy_version 1510 (0.0006) [2023-03-21 12:11:16,490][23264] Fps is (10 sec: 31539.2, 60 sec: 31402.6, 300 sec: 30270.4). Total num frames: 6205440. Throughput: 0: 7834.9. Samples: 1547066. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-03-21 12:11:16,490][23264] Avg episode reward: [(0, '28.077')] [2023-03-21 12:11:17,146][23362] Updated weights for policy 0, policy_version 1520 (0.0006) [2023-03-21 12:11:18,454][23362] Updated weights for policy 0, policy_version 1530 (0.0006) [2023-03-21 12:11:19,772][23362] Updated weights for policy 0, policy_version 1540 (0.0006) [2023-03-21 12:11:21,098][23362] Updated weights for policy 0, policy_version 1550 (0.0006) [2023-03-21 12:11:21,490][23264] Fps is (10 sec: 31539.2, 60 sec: 31402.7, 300 sec: 30290.9). Total num frames: 6361088. Throughput: 0: 7823.7. Samples: 1570438. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:11:21,490][23264] Avg episode reward: [(0, '27.708')] [2023-03-21 12:11:22,396][23362] Updated weights for policy 0, policy_version 1560 (0.0006) [2023-03-21 12:11:23,712][23362] Updated weights for policy 0, policy_version 1570 (0.0006) [2023-03-21 12:11:25,012][23362] Updated weights for policy 0, policy_version 1580 (0.0006) [2023-03-21 12:11:26,298][23362] Updated weights for policy 0, policy_version 1590 (0.0006) [2023-03-21 12:11:26,490][23264] Fps is (10 sec: 31129.7, 60 sec: 31334.4, 300 sec: 30310.4). Total num frames: 6516736. Throughput: 0: 7831.0. Samples: 1617628. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0) [2023-03-21 12:11:26,490][23264] Avg episode reward: [(0, '26.281')] [2023-03-21 12:11:27,615][23362] Updated weights for policy 0, policy_version 1600 (0.0006) [2023-03-21 12:11:28,923][23362] Updated weights for policy 0, policy_version 1610 (0.0006) [2023-03-21 12:11:30,220][23362] Updated weights for policy 0, policy_version 1620 (0.0006) [2023-03-21 12:11:31,489][23264] Fps is (10 sec: 31539.5, 60 sec: 31402.7, 300 sec: 30347.7). Total num frames: 6676480. Throughput: 0: 7810.1. Samples: 1664852. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:11:31,490][23264] Avg episode reward: [(0, '25.423')] [2023-03-21 12:11:31,490][23362] Updated weights for policy 0, policy_version 1630 (0.0006) [2023-03-21 12:11:32,806][23362] Updated weights for policy 0, policy_version 1640 (0.0006) [2023-03-21 12:11:34,123][23362] Updated weights for policy 0, policy_version 1650 (0.0007) [2023-03-21 12:11:35,453][23362] Updated weights for policy 0, policy_version 1660 (0.0007) [2023-03-21 12:11:36,490][23264] Fps is (10 sec: 31539.2, 60 sec: 31334.4, 300 sec: 30365.0). Total num frames: 6832128. Throughput: 0: 7821.6. Samples: 1688248. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-03-21 12:11:36,490][23264] Avg episode reward: [(0, '25.938')] [2023-03-21 12:11:36,742][23362] Updated weights for policy 0, policy_version 1670 (0.0006) [2023-03-21 12:11:38,046][23362] Updated weights for policy 0, policy_version 1680 (0.0006) [2023-03-21 12:11:39,343][23362] Updated weights for policy 0, policy_version 1690 (0.0006) [2023-03-21 12:11:40,646][23362] Updated weights for policy 0, policy_version 1700 (0.0007) [2023-03-21 12:11:41,489][23264] Fps is (10 sec: 31129.4, 60 sec: 31334.4, 300 sec: 30381.6). Total num frames: 6987776. Throughput: 0: 7827.4. Samples: 1735356. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-03-21 12:11:41,490][23264] Avg episode reward: [(0, '26.805')] [2023-03-21 12:11:41,941][23362] Updated weights for policy 0, policy_version 1710 (0.0007) [2023-03-21 12:11:43,229][23362] Updated weights for policy 0, policy_version 1720 (0.0006) [2023-03-21 12:11:44,518][23362] Updated weights for policy 0, policy_version 1730 (0.0006) [2023-03-21 12:11:45,793][23362] Updated weights for policy 0, policy_version 1740 (0.0006) [2023-03-21 12:11:46,489][23264] Fps is (10 sec: 31539.3, 60 sec: 31334.4, 300 sec: 30415.0). Total num frames: 7147520. Throughput: 0: 7845.4. Samples: 1782870. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2023-03-21 12:11:46,490][23264] Avg episode reward: [(0, '24.793')] [2023-03-21 12:11:46,493][23332] Saving /home/antpc/Desktop/rl_course/train_dir/default_experiment/checkpoint_p0/checkpoint_000001745_7147520.pth... [2023-03-21 12:11:47,126][23362] Updated weights for policy 0, policy_version 1750 (0.0006) [2023-03-21 12:11:48,461][23362] Updated weights for policy 0, policy_version 1760 (0.0007) [2023-03-21 12:11:49,773][23362] Updated weights for policy 0, policy_version 1770 (0.0007) [2023-03-21 12:11:51,095][23362] Updated weights for policy 0, policy_version 1780 (0.0007) [2023-03-21 12:11:51,490][23264] Fps is (10 sec: 31539.1, 60 sec: 31334.4, 300 sec: 30429.9). Total num frames: 7303168. Throughput: 0: 7826.7. Samples: 1805876. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:11:51,490][23264] Avg episode reward: [(0, '25.244')] [2023-03-21 12:11:52,389][23362] Updated weights for policy 0, policy_version 1790 (0.0007) [2023-03-21 12:11:53,710][23362] Updated weights for policy 0, policy_version 1800 (0.0006) [2023-03-21 12:11:55,003][23362] Updated weights for policy 0, policy_version 1810 (0.0007) [2023-03-21 12:11:56,304][23362] Updated weights for policy 0, policy_version 1820 (0.0007) [2023-03-21 12:11:56,489][23264] Fps is (10 sec: 31129.6, 60 sec: 31334.4, 300 sec: 30444.2). Total num frames: 7458816. Throughput: 0: 7835.7. Samples: 1852768. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:11:56,490][23264] Avg episode reward: [(0, '23.761')] [2023-03-21 12:11:57,571][23362] Updated weights for policy 0, policy_version 1830 (0.0006) [2023-03-21 12:11:58,883][23362] Updated weights for policy 0, policy_version 1840 (0.0007) [2023-03-21 12:12:00,179][23362] Updated weights for policy 0, policy_version 1850 (0.0007) [2023-03-21 12:12:01,462][23362] Updated weights for policy 0, policy_version 1860 (0.0006) [2023-03-21 12:12:01,490][23264] Fps is (10 sec: 31539.2, 60 sec: 31402.7, 300 sec: 30474.2). Total num frames: 7618560. Throughput: 0: 7847.4. Samples: 1900198. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:12:01,490][23264] Avg episode reward: [(0, '26.039')] [2023-03-21 12:12:02,775][23362] Updated weights for policy 0, policy_version 1870 (0.0007) [2023-03-21 12:12:04,067][23362] Updated weights for policy 0, policy_version 1880 (0.0006) [2023-03-21 12:12:05,354][23362] Updated weights for policy 0, policy_version 1890 (0.0006) [2023-03-21 12:12:06,490][23264] Fps is (10 sec: 31539.1, 60 sec: 31402.7, 300 sec: 30487.1). Total num frames: 7774208. Throughput: 0: 7855.2. Samples: 1923924. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-03-21 12:12:06,490][23264] Avg episode reward: [(0, '26.110')] [2023-03-21 12:12:06,652][23362] Updated weights for policy 0, policy_version 1900 (0.0006) [2023-03-21 12:12:07,929][23362] Updated weights for policy 0, policy_version 1910 (0.0006) [2023-03-21 12:12:09,218][23362] Updated weights for policy 0, policy_version 1920 (0.0007) [2023-03-21 12:12:10,504][23362] Updated weights for policy 0, policy_version 1930 (0.0006) [2023-03-21 12:12:11,489][23264] Fps is (10 sec: 31539.3, 60 sec: 31470.9, 300 sec: 30515.2). Total num frames: 7933952. Throughput: 0: 7869.9. Samples: 1971772. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-03-21 12:12:11,490][23264] Avg episode reward: [(0, '24.129')] [2023-03-21 12:12:11,801][23362] Updated weights for policy 0, policy_version 1940 (0.0007) [2023-03-21 12:12:13,064][23362] Updated weights for policy 0, policy_version 1950 (0.0006) [2023-03-21 12:12:13,720][23264] Component Batcher_0 stopped! [2023-03-21 12:12:13,720][23332] Stopping Batcher_0... [2023-03-21 12:12:13,720][23332] Loop batcher_evt_loop terminating... [2023-03-21 12:12:13,720][23332] Saving /home/antpc/Desktop/rl_course/train_dir/default_experiment/checkpoint_p0/checkpoint_000001955_8007680.pth... [2023-03-21 12:12:13,726][23364] Stopping RolloutWorker_w2... [2023-03-21 12:12:13,726][23264] Component RolloutWorker_w2 stopped! [2023-03-21 12:12:13,726][23384] Stopping RolloutWorker_w7... [2023-03-21 12:12:13,726][23361] Stopping RolloutWorker_w0... [2023-03-21 12:12:13,726][23364] Loop rollout_proc2_evt_loop terminating... [2023-03-21 12:12:13,726][23384] Loop rollout_proc7_evt_loop terminating... [2023-03-21 12:12:13,726][23361] Loop rollout_proc0_evt_loop terminating... [2023-03-21 12:12:13,726][23264] Component RolloutWorker_w7 stopped! [2023-03-21 12:12:13,726][23366] Stopping RolloutWorker_w4... [2023-03-21 12:12:13,726][23264] Component RolloutWorker_w0 stopped! [2023-03-21 12:12:13,726][23363] Stopping RolloutWorker_w1... [2023-03-21 12:12:13,726][23367] Stopping RolloutWorker_w5... [2023-03-21 12:12:13,727][23264] Component RolloutWorker_w4 stopped! [2023-03-21 12:12:13,727][23366] Loop rollout_proc4_evt_loop terminating... [2023-03-21 12:12:13,727][23264] Component RolloutWorker_w1 stopped! [2023-03-21 12:12:13,727][23367] Loop rollout_proc5_evt_loop terminating... [2023-03-21 12:12:13,727][23363] Loop rollout_proc1_evt_loop terminating... [2023-03-21 12:12:13,727][23264] Component RolloutWorker_w5 stopped! [2023-03-21 12:12:13,733][23365] Stopping RolloutWorker_w3... [2023-03-21 12:12:13,733][23264] Component RolloutWorker_w3 stopped! [2023-03-21 12:12:13,733][23365] Loop rollout_proc3_evt_loop terminating... [2023-03-21 12:12:13,734][23362] Weights refcount: 2 0 [2023-03-21 12:12:13,735][23362] Stopping InferenceWorker_p0-w0... [2023-03-21 12:12:13,735][23264] Component InferenceWorker_p0-w0 stopped! [2023-03-21 12:12:13,735][23362] Loop inference_proc0-0_evt_loop terminating... [2023-03-21 12:12:13,737][23383] Stopping RolloutWorker_w6... [2023-03-21 12:12:13,737][23264] Component RolloutWorker_w6 stopped! [2023-03-21 12:12:13,738][23383] Loop rollout_proc6_evt_loop terminating... [2023-03-21 12:12:13,764][23332] Removing /home/antpc/Desktop/rl_course/train_dir/default_experiment/checkpoint_p0/checkpoint_000000835_3420160.pth [2023-03-21 12:12:13,769][23332] Saving /home/antpc/Desktop/rl_course/train_dir/default_experiment/checkpoint_p0/checkpoint_000001955_8007680.pth... [2023-03-21 12:12:13,835][23332] Stopping LearnerWorker_p0... [2023-03-21 12:12:13,835][23264] Component LearnerWorker_p0 stopped! [2023-03-21 12:12:13,836][23332] Loop learner_proc0_evt_loop terminating... [2023-03-21 12:12:13,836][23264] Waiting for process learner_proc0 to stop... [2023-03-21 12:12:14,433][23264] Waiting for process inference_proc0-0 to join... [2023-03-21 12:12:14,433][23264] Waiting for process rollout_proc0 to join... [2023-03-21 12:12:14,434][23264] Waiting for process rollout_proc1 to join... [2023-03-21 12:12:14,434][23264] Waiting for process rollout_proc2 to join... [2023-03-21 12:12:14,434][23264] Waiting for process rollout_proc3 to join... [2023-03-21 12:12:14,434][23264] Waiting for process rollout_proc4 to join... [2023-03-21 12:12:14,434][23264] Waiting for process rollout_proc5 to join... [2023-03-21 12:12:14,434][23264] Waiting for process rollout_proc6 to join... [2023-03-21 12:12:14,434][23264] Waiting for process rollout_proc7 to join... [2023-03-21 12:12:14,434][23264] Batcher 0 profile tree view: batching: 13.5546, releasing_batches: 0.0305 [2023-03-21 12:12:14,435][23264] InferenceWorker_p0-w0 profile tree view: wait_policy: 0.0000 wait_policy_total: 3.8298 update_model: 3.9375 weight_update: 0.0007 one_step: 0.0019 handle_policy_step: 240.2654 deserialize: 10.3571, stack: 1.3895, obs_to_device_normalize: 62.6308, forward: 103.8704, send_messages: 14.3477 prepare_outputs: 38.2395 to_cpu: 26.8497 [2023-03-21 12:12:14,435][23264] Learner 0 profile tree view: misc: 0.0074, prepare_batch: 12.9347 train: 38.8463 epoch_init: 0.0068, minibatch_init: 0.0089, losses_postprocess: 0.2985, kl_divergence: 0.2877, after_optimizer: 10.1432 calculate_losses: 17.7054 losses_init: 0.0038, forward_head: 1.2325, bptt_initial: 12.7581, tail: 0.6975, advantages_returns: 0.1941, losses: 1.2239 bptt: 1.3730 bptt_forward_core: 1.3189 update: 9.9149 clip: 1.2032 [2023-03-21 12:12:14,435][23264] RolloutWorker_w0 profile tree view: wait_for_trajectories: 0.1727, enqueue_policy_requests: 10.1575, env_step: 130.0109, overhead: 11.9517, complete_rollouts: 0.3140 save_policy_outputs: 10.6131 split_output_tensors: 5.2450 [2023-03-21 12:12:14,435][23264] RolloutWorker_w7 profile tree view: wait_for_trajectories: 0.1715, enqueue_policy_requests: 10.4371, env_step: 132.1329, overhead: 12.2068, complete_rollouts: 0.3086 save_policy_outputs: 10.8866 split_output_tensors: 5.3324 [2023-03-21 12:12:14,435][23264] Loop Runner_EvtLoop terminating... [2023-03-21 12:12:14,435][23264] Runner profile tree view: main_loop: 266.4156 [2023-03-21 12:12:14,435][23264] Collected {0: 8007680}, FPS: 30057.1 [2023-03-21 12:12:14,440][23264] Loading existing experiment configuration from /home/antpc/Desktop/rl_course/train_dir/default_experiment/config.json [2023-03-21 12:12:14,440][23264] Overriding arg 'num_workers' with value 1 passed from command line [2023-03-21 12:12:14,440][23264] Adding new argument 'no_render'=True that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'save_video'=True that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'video_name'=None that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'push_to_hub'=False that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'hf_repository'=None that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'policy_index'=0 that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'train_script'=None that is not in the saved config file! [2023-03-21 12:12:14,440][23264] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2023-03-21 12:12:14,441][23264] Using frameskip 1 and render_action_repeat=4 for evaluation [2023-03-21 12:12:14,445][23264] Doom resolution: 160x120, resize resolution: (128, 72) [2023-03-21 12:12:14,446][23264] RunningMeanStd input shape: (3, 72, 128) [2023-03-21 12:12:14,446][23264] RunningMeanStd input shape: (1,) [2023-03-21 12:12:14,453][23264] ConvEncoder: input_channels=3 [2023-03-21 12:12:14,539][23264] Conv encoder output size: 512 [2023-03-21 12:12:14,539][23264] Policy head output size: 512 [2023-03-21 12:12:15,798][23264] Loading state from checkpoint /home/antpc/Desktop/rl_course/train_dir/default_experiment/checkpoint_p0/checkpoint_000001955_8007680.pth... [2023-03-21 12:12:16,386][23264] Num frames 100... [2023-03-21 12:12:16,443][23264] Num frames 200... [2023-03-21 12:12:16,502][23264] Num frames 300... [2023-03-21 12:12:16,560][23264] Num frames 400... [2023-03-21 12:12:16,619][23264] Num frames 500... [2023-03-21 12:12:16,677][23264] Num frames 600... [2023-03-21 12:12:16,754][23264] Avg episode rewards: #0: 12.370, true rewards: #0: 6.370 [2023-03-21 12:12:16,754][23264] Avg episode reward: 12.370, avg true_objective: 6.370 [2023-03-21 12:12:16,793][23264] Num frames 700... [2023-03-21 12:12:16,849][23264] Num frames 800... [2023-03-21 12:12:16,905][23264] Num frames 900... [2023-03-21 12:12:17,008][23264] Avg episode rewards: #0: 8.445, true rewards: #0: 4.945 [2023-03-21 12:12:17,009][23264] Avg episode reward: 8.445, avg true_objective: 4.945 [2023-03-21 12:12:17,018][23264] Num frames 1000... [2023-03-21 12:12:17,076][23264] Num frames 1100... [2023-03-21 12:12:17,133][23264] Num frames 1200... [2023-03-21 12:12:17,189][23264] Num frames 1300... [2023-03-21 12:12:17,251][23264] Num frames 1400... [2023-03-21 12:12:17,313][23264] Num frames 1500... [2023-03-21 12:12:17,377][23264] Num frames 1600... [2023-03-21 12:12:17,436][23264] Num frames 1700... [2023-03-21 12:12:17,496][23264] Num frames 1800... [2023-03-21 12:12:17,553][23264] Num frames 1900... [2023-03-21 12:12:17,609][23264] Num frames 2000... [2023-03-21 12:12:17,666][23264] Num frames 2100... [2023-03-21 12:12:17,723][23264] Num frames 2200... [2023-03-21 12:12:17,780][23264] Num frames 2300... [2023-03-21 12:12:17,838][23264] Num frames 2400... [2023-03-21 12:12:17,895][23264] Num frames 2500... [2023-03-21 12:12:17,963][23264] Avg episode rewards: #0: 18.753, true rewards: #0: 8.420 [2023-03-21 12:12:17,963][23264] Avg episode reward: 18.753, avg true_objective: 8.420 [2023-03-21 12:12:18,007][23264] Num frames 2600... [2023-03-21 12:12:18,066][23264] Num frames 2700... [2023-03-21 12:12:18,124][23264] Num frames 2800... [2023-03-21 12:12:18,181][23264] Num frames 2900... [2023-03-21 12:12:18,238][23264] Num frames 3000... [2023-03-21 12:12:18,297][23264] Num frames 3100... [2023-03-21 12:12:18,354][23264] Num frames 3200... [2023-03-21 12:12:18,412][23264] Num frames 3300... [2023-03-21 12:12:18,469][23264] Num frames 3400... [2023-03-21 12:12:18,536][23264] Avg episode rewards: #0: 18.805, true rewards: #0: 8.555 [2023-03-21 12:12:18,536][23264] Avg episode reward: 18.805, avg true_objective: 8.555 [2023-03-21 12:12:18,590][23264] Num frames 3500... [2023-03-21 12:12:18,647][23264] Num frames 3600... [2023-03-21 12:12:18,704][23264] Num frames 3700... [2023-03-21 12:12:18,763][23264] Num frames 3800... [2023-03-21 12:12:18,822][23264] Num frames 3900... [2023-03-21 12:12:18,880][23264] Num frames 4000... [2023-03-21 12:12:18,938][23264] Num frames 4100... [2023-03-21 12:12:18,996][23264] Num frames 4200... [2023-03-21 12:12:19,055][23264] Num frames 4300... [2023-03-21 12:12:19,114][23264] Num frames 4400... [2023-03-21 12:12:19,172][23264] Num frames 4500... [2023-03-21 12:12:19,230][23264] Num frames 4600... [2023-03-21 12:12:19,288][23264] Num frames 4700... [2023-03-21 12:12:19,347][23264] Num frames 4800... [2023-03-21 12:12:19,407][23264] Num frames 4900... [2023-03-21 12:12:19,466][23264] Num frames 5000... [2023-03-21 12:12:19,524][23264] Num frames 5100... [2023-03-21 12:12:19,583][23264] Num frames 5200... [2023-03-21 12:12:19,642][23264] Num frames 5300... [2023-03-21 12:12:19,701][23264] Num frames 5400... [2023-03-21 12:12:19,760][23264] Num frames 5500... [2023-03-21 12:12:19,826][23264] Avg episode rewards: #0: 26.644, true rewards: #0: 11.044 [2023-03-21 12:12:19,826][23264] Avg episode reward: 26.644, avg true_objective: 11.044 [2023-03-21 12:12:19,873][23264] Num frames 5600... [2023-03-21 12:12:19,933][23264] Num frames 5700... [2023-03-21 12:12:19,991][23264] Num frames 5800... [2023-03-21 12:12:20,049][23264] Num frames 5900... [2023-03-21 12:12:20,106][23264] Num frames 6000... [2023-03-21 12:12:20,163][23264] Num frames 6100... [2023-03-21 12:12:20,221][23264] Num frames 6200... [2023-03-21 12:12:20,279][23264] Num frames 6300... [2023-03-21 12:12:20,337][23264] Num frames 6400... [2023-03-21 12:12:20,394][23264] Num frames 6500... [2023-03-21 12:12:20,451][23264] Num frames 6600... [2023-03-21 12:12:20,548][23264] Avg episode rewards: #0: 26.623, true rewards: #0: 11.123 [2023-03-21 12:12:20,548][23264] Avg episode reward: 26.623, avg true_objective: 11.123 [2023-03-21 12:12:20,570][23264] Num frames 6700... [2023-03-21 12:12:20,627][23264] Num frames 6800... [2023-03-21 12:12:20,685][23264] Num frames 6900... [2023-03-21 12:12:20,742][23264] Num frames 7000... [2023-03-21 12:12:20,799][23264] Num frames 7100... [2023-03-21 12:12:20,857][23264] Num frames 7200... [2023-03-21 12:12:20,914][23264] Num frames 7300... [2023-03-21 12:12:20,972][23264] Num frames 7400... [2023-03-21 12:12:21,031][23264] Num frames 7500... [2023-03-21 12:12:21,090][23264] Num frames 7600... [2023-03-21 12:12:21,144][23264] Avg episode rewards: #0: 26.003, true rewards: #0: 10.860 [2023-03-21 12:12:21,144][23264] Avg episode reward: 26.003, avg true_objective: 10.860 [2023-03-21 12:12:21,203][23264] Num frames 7700... [2023-03-21 12:12:21,262][23264] Num frames 7800... [2023-03-21 12:12:21,321][23264] Num frames 7900... [2023-03-21 12:12:21,379][23264] Num frames 8000... [2023-03-21 12:12:21,436][23264] Num frames 8100... [2023-03-21 12:12:21,493][23264] Num frames 8200... [2023-03-21 12:12:21,551][23264] Num frames 8300... [2023-03-21 12:12:21,609][23264] Num frames 8400... [2023-03-21 12:12:21,663][23264] Avg episode rewards: #0: 24.877, true rewards: #0: 10.502 [2023-03-21 12:12:21,663][23264] Avg episode reward: 24.877, avg true_objective: 10.502 [2023-03-21 12:12:21,721][23264] Num frames 8500... [2023-03-21 12:12:21,779][23264] Num frames 8600... [2023-03-21 12:12:21,837][23264] Num frames 8700... [2023-03-21 12:12:21,895][23264] Num frames 8800... [2023-03-21 12:12:21,953][23264] Num frames 8900... [2023-03-21 12:12:22,011][23264] Num frames 9000... [2023-03-21 12:12:22,075][23264] Num frames 9100... [2023-03-21 12:12:22,133][23264] Num frames 9200... [2023-03-21 12:12:22,191][23264] Num frames 9300... [2023-03-21 12:12:22,251][23264] Num frames 9400... [2023-03-21 12:12:22,310][23264] Num frames 9500... [2023-03-21 12:12:22,369][23264] Num frames 9600... [2023-03-21 12:12:22,428][23264] Num frames 9700... [2023-03-21 12:12:22,491][23264] Num frames 9800... [2023-03-21 12:12:22,549][23264] Num frames 9900... [2023-03-21 12:12:22,608][23264] Num frames 10000... [2023-03-21 12:12:22,667][23264] Num frames 10100... [2023-03-21 12:12:22,727][23264] Num frames 10200... [2023-03-21 12:12:22,813][23264] Avg episode rewards: #0: 28.284, true rewards: #0: 11.396 [2023-03-21 12:12:22,814][23264] Avg episode reward: 28.284, avg true_objective: 11.396 [2023-03-21 12:12:22,847][23264] Num frames 10300... [2023-03-21 12:12:22,905][23264] Num frames 10400... [2023-03-21 12:12:22,963][23264] Num frames 10500... [2023-03-21 12:12:23,028][23264] Num frames 10600... [2023-03-21 12:12:23,087][23264] Num frames 10700... [2023-03-21 12:12:23,144][23264] Num frames 10800... [2023-03-21 12:12:23,202][23264] Num frames 10900... [2023-03-21 12:12:23,261][23264] Num frames 11000... [2023-03-21 12:12:23,323][23264] Avg episode rewards: #0: 27.015, true rewards: #0: 11.015 [2023-03-21 12:12:23,323][23264] Avg episode reward: 27.015, avg true_objective: 11.015 [2023-03-21 12:12:35,089][23264] Replay video saved to /home/antpc/Desktop/rl_course/train_dir/default_experiment/replay.mp4!