0xid commited on
Commit
8f63906
·
1 Parent(s): 80bce16

Initial commit

Browse files
README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
- value: -7.54 +/- 3.28
20
  name: mean_reward
21
  verified: false
22
  ---
 
16
  type: PandaReachDense-v2
17
  metrics:
18
  - type: mean_reward
19
+ value: -0.75 +/- 0.20
20
  name: mean_reward
21
  verified: false
22
  ---
a2c-PandaReachDense-v2.zip CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:360e82154ad5760be821c4186947c13deb9a831e77326b7aac0d7542822618d0
3
- size 108023
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ccea356d18f73680f057b86fd5aed43e4cc6e9629a9c2f02fd1921be623f4c9d
3
+ size 109572
a2c-PandaReachDense-v2/_stable_baselines3_version CHANGED
@@ -1 +1 @@
1
- 1.7.0
 
1
+ 1.7.0a11
a2c-PandaReachDense-v2/data CHANGED
@@ -4,14 +4,16 @@
4
  ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
5
  "__module__": "stable_baselines3.common.policies",
6
  "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
7
- "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f9e559ce310>",
8
  "__abstractmethods__": "frozenset()",
9
- "_abc_impl": "<_abc_data object at 0x7f9e559cc1e0>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
13
  ":type:": "<class 'dict'>",
14
- ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
 
 
15
  "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
16
  "optimizer_kwargs": {
17
  "alpha": 0.99,
@@ -21,7 +23,7 @@
21
  },
22
  "observation_space": {
23
  ":type:": "<class 'gym.spaces.dict.Dict'>",
24
- ":serialized:": "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",
25
  "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
26
  "_shape": null,
27
  "dtype": null,
@@ -29,7 +31,7 @@
29
  },
30
  "action_space": {
31
  ":type:": "<class 'gym.spaces.box.Box'>",
32
- ":serialized:": "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",
33
  "dtype": "float32",
34
  "_shape": [
35
  3
@@ -41,24 +43,24 @@
41
  "_np_random": null
42
  },
43
  "n_envs": 4,
44
- "num_timesteps": 1000000,
45
- "_total_timesteps": 1000000,
46
  "_num_timesteps_at_start": 0,
47
  "seed": null,
48
  "action_noise": null,
49
- "start_time": 1675514743738389063,
50
- "learning_rate": 0.0007,
51
  "tensorboard_log": null,
52
  "lr_schedule": {
53
  ":type:": "<class 'function'>",
54
- ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
55
  },
56
  "_last_obs": {
57
  ":type:": "<class 'collections.OrderedDict'>",
58
- ":serialized:": "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",
59
- "achieved_goal": "[[ 0.49619576 -0.01847123 0.7252342 ]\n [ 0.49619576 -0.01847123 0.7252342 ]\n [ 0.49619576 -0.01847123 0.7252342 ]\n [ 0.49619576 -0.01847123 0.7252342 ]]",
60
- "desired_goal": "[[-0.69929326 -0.15935193 -0.531931 ]\n [-1.2433817 1.4337019 -1.095867 ]\n [ 1.6722722 0.47631875 0.04317093]\n [-0.10603809 -0.30770305 0.17555676]]",
61
- "observation": "[[ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]\n [ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]\n [ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]\n [ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]]"
62
  },
63
  "_last_episode_starts": {
64
  ":type:": "<class 'numpy.ndarray'>",
@@ -66,29 +68,29 @@
66
  },
67
  "_last_original_obs": {
68
  ":type:": "<class 'collections.OrderedDict'>",
69
- ":serialized:": "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",
70
  "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
71
- "desired_goal": "[[-0.07301767 -0.05448063 0.05645372]\n [-0.13530518 0.08670864 0.2989897 ]\n [ 0.05942765 0.05030496 0.15111321]\n [-0.01570116 0.11629818 0.19857652]]",
72
  "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
73
  },
74
  "_episode_num": 0,
75
- "use_sde": false,
76
  "sde_sample_freq": -1,
77
  "_current_progress_remaining": 0.0,
78
  "ep_info_buffer": {
79
  ":type:": "<class 'collections.deque'>",
80
- ":serialized:": "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"
81
  },
82
  "ep_success_buffer": {
83
  ":type:": "<class 'collections.deque'>",
84
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
85
  },
86
- "_n_updates": 50000,
87
- "n_steps": 5,
88
  "gamma": 0.99,
89
- "gae_lambda": 1.0,
90
  "ent_coef": 0.0,
91
- "vf_coef": 0.5,
92
  "max_grad_norm": 0.5,
93
  "normalize_advantage": false
94
  }
 
4
  ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
5
  "__module__": "stable_baselines3.common.policies",
6
  "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
7
+ "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f9909a91480>",
8
  "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x7f9909a8d3c0>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
13
  ":type:": "<class 'dict'>",
14
+ ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
15
+ "log_std_init": -2,
16
+ "ortho_init": false,
17
  "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
18
  "optimizer_kwargs": {
19
  "alpha": 0.99,
 
23
  },
24
  "observation_space": {
25
  ":type:": "<class 'gym.spaces.dict.Dict'>",
26
+ ":serialized:": "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",
27
  "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
28
  "_shape": null,
29
  "dtype": null,
 
31
  },
32
  "action_space": {
33
  ":type:": "<class 'gym.spaces.box.Box'>",
34
+ ":serialized:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==",
35
  "dtype": "float32",
36
  "_shape": [
37
  3
 
43
  "_np_random": null
44
  },
45
  "n_envs": 4,
46
+ "num_timesteps": 2000000,
47
+ "_total_timesteps": 2000000,
48
  "_num_timesteps_at_start": 0,
49
  "seed": null,
50
  "action_noise": null,
51
+ "start_time": 1675633533958835227,
52
+ "learning_rate": 0.00096,
53
  "tensorboard_log": null,
54
  "lr_schedule": {
55
  ":type:": "<class 'function'>",
56
+ ":serialized:": "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"
57
  },
58
  "_last_obs": {
59
  ":type:": "<class 'collections.OrderedDict'>",
60
+ ":serialized:": "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",
61
+ "achieved_goal": "[[0.43100655 0.00353011 0.5488614 ]\n [0.43100655 0.00353011 0.5488614 ]\n [0.43100655 0.00353011 0.5488614 ]\n [0.43100655 0.00353011 0.5488614 ]]",
62
+ "desired_goal": "[[ 1.7039208 -0.09968922 -1.2111995 ]\n [-0.07452689 -0.00474142 0.6391956 ]\n [-1.7242658 0.3186322 -1.7232518 ]\n [ 1.1203051 -0.21685098 1.7131268 ]]",
63
+ "observation": "[[0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]\n [0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]\n [0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]\n [0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]]"
64
  },
65
  "_last_episode_starts": {
66
  ":type:": "<class 'numpy.ndarray'>",
 
68
  },
69
  "_last_original_obs": {
70
  ":type:": "<class 'collections.OrderedDict'>",
71
+ ":serialized:": "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",
72
  "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
73
+ "desired_goal": "[[-0.03459626 -0.02390785 0.26067808]\n [ 0.03380011 0.03689701 0.03735084]\n [ 0.14025328 0.0018939 0.03306283]\n [-0.01889866 -0.13171726 0.05509558]]",
74
  "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
75
  },
76
  "_episode_num": 0,
77
+ "use_sde": true,
78
  "sde_sample_freq": -1,
79
  "_current_progress_remaining": 0.0,
80
  "ep_info_buffer": {
81
  ":type:": "<class 'collections.deque'>",
82
+ ":serialized:": "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"
83
  },
84
  "ep_success_buffer": {
85
  ":type:": "<class 'collections.deque'>",
86
  ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
87
  },
88
+ "_n_updates": 62500,
89
+ "n_steps": 8,
90
  "gamma": 0.99,
91
+ "gae_lambda": 0.9,
92
  "ent_coef": 0.0,
93
+ "vf_coef": 0.4,
94
  "max_grad_norm": 0.5,
95
  "normalize_advantage": false
96
  }
a2c-PandaReachDense-v2/policy.optimizer.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:44890403adc5a0ed9500a467139e5df0aacaaa3aea9f4acf9a289d977d8d8441
3
- size 44734
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5866e684a79c01bf30e3e3b93197fdd6bf7de89c8c942401bc44bf525a7f9e6f
3
+ size 45438
a2c-PandaReachDense-v2/policy.pth CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:357d56237ffdf53f1160849b4b50ece8486e59c5a81bcd4044c5307a867e8224
3
- size 46014
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6ab567b59fe2aa23d7def848697be60b49ca10387248d29d7b75c3da1e369169
3
+ size 46718
a2c-PandaReachDense-v2/system_info.txt CHANGED
@@ -1,7 +1,7 @@
1
- - OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
- - Python: 3.8.10
3
- - Stable-Baselines3: 1.7.0
4
- - PyTorch: 1.13.1+cu116
5
  - GPU Enabled: True
6
- - Numpy: 1.21.6
7
  - Gym: 0.21.0
 
1
+ - OS: Linux-5.4.0-104-generic-x86_64-with-glibc2.27 # 118~18.04.1-Ubuntu SMP Thu Mar 3 13:53:15 UTC 2022
2
+ - Python: 3.10.9+
3
+ - Stable-Baselines3: 1.7.0a11
4
+ - PyTorch: 1.13.1+cu117
5
  - GPU Enabled: True
6
+ - Numpy: 1.24.1
7
  - Gym: 0.21.0
config.json CHANGED
@@ -1 +1 @@
1
- {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f9e559ce310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9e559cc1e0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675514743738389063, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.49619576 -0.01847123 0.7252342 ]\n [ 0.49619576 -0.01847123 0.7252342 ]\n [ 0.49619576 -0.01847123 0.7252342 ]\n [ 0.49619576 -0.01847123 0.7252342 ]]", "desired_goal": "[[-0.69929326 -0.15935193 -0.531931 ]\n [-1.2433817 1.4337019 -1.095867 ]\n [ 1.6722722 0.47631875 0.04317093]\n [-0.10603809 -0.30770305 0.17555676]]", "observation": "[[ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]\n [ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]\n [ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]\n [ 0.49619576 -0.01847123 0.7252342 0.03554272 -0.00521849 0.0271677 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[-0.07301767 -0.05448063 0.05645372]\n [-0.13530518 0.08670864 0.2989897 ]\n [ 0.05942765 0.05030496 0.15111321]\n [-0.01570116 0.11629818 0.19857652]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
 
1
+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f9909a91480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9909a8d3c0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "gAWVWAMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZSMAUOUdJRSlIwEaGlnaJRoHiiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBZLA4WUaCF0lFKUjA1ib3VuZGVkX2JlbG93lGgeKJYDAAAAAAAAAAEBAZRoE4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIXSUUpSMDWJvdW5kZWRfYWJvdmWUaB4olgMAAAAAAAAAAQEBlGgtSwOFlGghdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBZoGUsDhZRoG2geKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZRoIXSUUpRoJGgeKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFksDhZRoIXSUUpRoKWgeKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoM2geKJYDAAAAAAAAAAEBAZRoLUsDhZRoIXSUUpRoOE51YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgWaBlLBoWUaBtoHiiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBZLBoWUaCF0lFKUaCRoHiiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBZLBoWUaCF0lFKUaCloHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDNoHiiWBgAAAAAAAAABAQEBAQGUaC1LBoWUaCF0lFKUaDhOdWJ1aBlOaBBOaDhOdWIu", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675633533958835227, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.43100655 0.00353011 0.5488614 ]\n [0.43100655 0.00353011 0.5488614 ]\n [0.43100655 0.00353011 0.5488614 ]\n [0.43100655 0.00353011 0.5488614 ]]", "desired_goal": "[[ 1.7039208 -0.09968922 -1.2111995 ]\n [-0.07452689 -0.00474142 0.6391956 ]\n [-1.7242658 0.3186322 -1.7232518 ]\n [ 1.1203051 -0.21685098 1.7131268 ]]", "observation": "[[0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]\n [0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]\n [0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]\n [0.43100655 0.00353011 0.5488614 0.06079813 0.00078644 0.05608419]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[-0.03459626 -0.02390785 0.26067808]\n [ 0.03380011 0.03689701 0.03735084]\n [ 0.14025328 0.0018939 0.03306283]\n [-0.01889866 -0.13171726 0.05509558]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.4.0-104-generic-x86_64-with-glibc2.27 # 118~18.04.1-Ubuntu SMP Thu Mar 3 13:53:15 UTC 2022", "Python": "3.10.9+", "Stable-Baselines3": "1.7.0a11", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.1", "Gym": "0.21.0"}}
replay.mp4 CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
 
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": -7.543660880997777, "std_reward": 3.2789337834297503, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-04T13:38:55.148402"}
 
1
+ {"mean_reward": -0.7502267611911521, "std_reward": 0.19893910968977355, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-06T00:07:39.842060"}
vec_normalize.pkl CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:6d4e00a5f082d1d49f8fe71596bbfe82c57c60e920ae8d31828b5f8d9ba2c826
3
- size 3056
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:39fa1835a737bffb9836340ff8396cf53009ad381c76324ecd8428e2129f37d8
3
+ size 3117