Pushing the 2nd version of Lunar lander RL model to HF Hub

Lunar_Lander_VSrinivas.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c8dc60b7ee8281409dc8581bb5159d7142b27bcb2de8d5fb7b50a549bda293f3
-size 148734
+oid sha256:d60c3ec804f74f9ec7ea8d4383d2457bd13b56d740cf48e53f5dd4e15b6feca8
+size 148747

Lunar_Lander_VSrinivas/data

@@ -4,20 +4,20 @@
         ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7c3e26aab880>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c3e26aab910>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c3e26aab9a0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c3e26aaba30>",
-        "_build": "<function ActorCriticPolicy._build at 0x7c3e26aabac0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7c3e26aabb50>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c3e26aabbe0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c3e26aabc70>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7c3e26aabd00>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c3e26aabd90>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c3e26aabe20>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c3e26aabeb0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7a4a48c18dc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a4a48c18e50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a4a48c18ee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a4a48c18f70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7a4a48c19000>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7a4a48c19090>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a4a48c19120>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a4a48c191b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7a4a48c19240>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a4a48c192d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a4a48c19360>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a4a48c193f0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7c3e26a52a80>"
+        "_abc_impl": "<_abc._abc_data object at 0x7a4a48bb5b00>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -26,16 +26,16 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1702052705878729935,
+    "start_time": 1702201909113882229,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+        ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
     },
     "_last_original_obs": null,
     "_episode_num": 0,
@@ -45,7 +45,7 @@
     "_stats_window_size": 100,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -83,7 +83,7 @@
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 32,
+    "batch_size": 128,
     "n_epochs": 8,
     "clip_range": {
         ":type:": "<class 'function'>",

Lunar_Lander_VSrinivas/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c382754afa3ddb0eecb0fbfc72331443cefb7036e5744a65c57e628d1361eb77
+oid sha256:a171d844aacb7d22eea2101259c6c700ead7eac7ac611423c04d0cce0329e157
 size 88362

Lunar_Lander_VSrinivas/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7ab15588dd180875b22a7ed8c607f9242b9c083736aef08a128d9e4acddc0a77
+oid sha256:d44c37c4f43024ecc485ef2bde5428d20dc7735518f272bcaebca55c118cbdea
 size 43762

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 276.44 +/- 18.22
+      value: 250.26 +/- 29.63
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7c3e26aab880>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c3e26aab910>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c3e26aab9a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c3e26aaba30>", "_build": "<function ActorCriticPolicy._build at 0x7c3e26aabac0>", "forward": "<function ActorCriticPolicy.forward at 0x7c3e26aabb50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c3e26aabbe0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c3e26aabc70>", "_predict": "<function ActorCriticPolicy._predict at 0x7c3e26aabd00>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c3e26aabd90>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c3e26aabe20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c3e26aabeb0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c3e26a52a80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1702052705878729935, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 32, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7a4a48c18dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a4a48c18e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a4a48c18ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a4a48c18f70>", "_build": "<function ActorCriticPolicy._build at 0x7a4a48c19000>", "forward": "<function ActorCriticPolicy.forward at 0x7a4a48c19090>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a4a48c19120>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a4a48c191b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7a4a48c19240>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a4a48c192d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a4a48c19360>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a4a48c193f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a4a48bb5b00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1702201909113882229, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVPAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQDyklF+d9UmMAWyUS+eMAXSUR0CRrV6shgVodX2UKGgGR0Bl2AoLG7z1aAdN6ANoCEdAka5PysjmjnV9lChoBkdAYurDk2gnMWgHTegDaAhHQJGxAjps41h1fZQoaAZHQGCegBtDUmVoB03oA2gIR0CRucLPD50sdX2UKGgGR0BgiYZflZHNaAdN6ANoCEdAkbsvicXm/3V9lChoBkdAYW/wl0HQhWgHTegDaAhHQJG+TPE87p51fZQoaAZHQGBk/eDWbw1oB03oA2gIR0CRwH5FPSDzdX2UKGgGR0BlIcfDDTBqaAdN6ANoCEdAkcbglSjxkXV9lChoBkdAQl9ZRsMy8GgHS8loCEdAkcfgt8NQTHV9lChoBkdAXolg0CRwImgHTegDaAhHQJHL2AlOXVt1fZQoaAZHQGZX80DU3GZoB03oA2gIR0CRzBEHMUypdX2UKGgGR0BmBoCQtBfKaAdN6ANoCEdAkc2ydat9yHV9lChoBkdAYHdBkZrHl2gHTegDaAhHQJHRiaoddVx1fZQoaAZHQGQ3RqwhW5poB03oA2gIR0CR0kWAf+0gdX2UKGgGR0BmZY4jrzGxaAdN6ANoCEdAkdY/2GqPwXV9lChoBkdAYH5B1LamGmgHTegDaAhHQJHXEbXHzYp1fZQoaAZHQGGjMdDIBBBoB03oA2gIR0CR2unWJ79idX2UKGgGR0BgzF56dDpkaAdN6ANoCEdAkeLElE7W/nV9lChoBkdAZL3l7MPjGWgHTegDaAhHQJHkDtD2Jzl1fZQoaAZHQGfv3ZXdTHdoB03oA2gIR0CR6Dq3EyckdX2UKGgGR0BlJ7gXMyJsaAdN6ANoCEdAkeg9xAB1cXV9lChoBkdAYjE66J66a2gHTegDaAhHQJHoxjCpFTh1fZQoaAZHQF+EqMFUyYZoB03oA2gIR0CR8sksSTQmdX2UKGgGR0Bmx+GVRk3CaAdN6ANoCEdAkfZAevIOpnV9lChoBkdAYsfsj3VTaWgHTegDaAhHQJH22SB9Tgl1fZQoaAZHQGCpSgPEsJ9oB03oA2gIR0CR9tPKMefadX2UKGgGR0BlJpaFEiMYaAdN6ANoCEdAkfu5NO/L1XV9lChoBkdAYTbF85S3s2gHTegDaAhHQJIAfV7Qb+91fZQoaAZHQCMUaya/h2poB0v2aAhHQJIpquNgjQl1fZQoaAZHQGKNM+u/1xtoB03oA2gIR0CSK5iI+GGmdX2UKGgGR0BlKk/6fra/aAdN6ANoCEdAkjPkx/NJOHV9lChoBkdATcAd6sySFGgHS9FoCEdAkjT3gccU/XV9lChoBkdAYmsAI6bONmgHTegDaAhHQJI9B1fVqet1fZQoaAZHQGTRACfYjB5oB03oA2gIR0CSQBU8mrsCdX2UKGgGR0BkxJ66asp5aAdN6ANoCEdAkkxtq1w5vXV9lChoBkdAZSmFB6a9b2gHTegDaAhHQJJRqNXHR1J1fZQoaAZHQGNwkGRmseZoB03oA2gIR0CSVPu+AVfvdX2UKGgGR0BjIcefZmI1aAdN6ANoCEdAkl0hwQ176nV9lChoBkdAYzjj2Bas62gHTegDaAhHQJJeeP6sQup1fZQoaAZHQGJd9Vmz0H1oB03oA2gIR0CSYXeZG8VYdX2UKGgGR0Bhopje9Ba+aAdN6ANoCEdAkmNr7Kq4pnV9lChoBkdAZ3FtIClrM2gHTegDaAhHQJJps7Rv3rV1fZQoaAZHQGZgsTewcHZoB03oA2gIR0CSas7qIJqqdX2UKGgGR0BlSbX8O09haAdN6ANoCEdAkm8GvOhTO3V9lChoBkdAYiBbdJrckGgHTegDaAhHQJJvRPykKu11fZQoaAZHQGJ4U5lvqC9oB03oA2gIR0CScPH6uW8idX2UKGgGR0Bj3rRYzSCwaAdN6ANoCEdAknTaYVqN63V9lChoBkdAY7p1/Ue+22gHTegDaAhHQJJ1qZlWfbt1fZQoaAZHQGZP0+9rXUZoB03oA2gIR0CSeU717IDHdX2UKGgGR0BfVW7nPmgbaAdN6ANoCEdAknoTtCzC13V9lChoBkdAYCXjy4FzMmgHTegDaAhHQJJ941KoQ4F1fZQoaAZHQGR6Sg5BC2NoB03oA2gIR0CSheDNhVlxdX2UKGgGR0Bk1oBV+7UYaAdN6ANoCEdAkoehb8m8d3V9lChoBkdAYnrDLKV6eGgHTegDaAhHQJKLzqqwQlN1fZQoaAZHQGPcDqfOD8NoB03oA2gIR0CSjF9ZzPrwdX2UKGgGR0Bkwt5v99+gaAdN6ANoCEdAkpYLz5GjK3V9lChoBkdAYyZVXFLnLmgHTegDaAhHQJKZTv2GqPx1fZQoaAZHQGS1+RHPNV1oB03oA2gIR0CSmeDaGpMpdX2UKGgGR0Bg7G9cry2AaAdN6ANoCEdAkp6fAO8TSXV9lChoBkdAYojaPCEYfmgHTegDaAhHQJKjUZgogFJ1fZQoaAZHQGHIiOmzjWFoB03oA2gIR0CSzUBU70WedX2UKGgGR0BjHvMdLg4waAdN6ANoCEdAks8C/bj943V9lChoBkdAYYo+L3sXzmgHTegDaAhHQJLXcLeANG51fZQoaAZHQGDys9KVY6poB03oA2gIR0CS2ICU5dWydX2UKGgGR0BkcQGB4D9waAdN6ANoCEdAkuBsB+4LC3V9lChoBkdAYgidLg4wRGgHTegDaAhHQJLjdXIU8FJ1fZQoaAZHQErbat9x6v9oB0v5aAhHQJLoi56MR6F1fZQoaAZHQFrxOclPactoB03oA2gIR0CS71yKNyYHdX2UKGgGR0BfnjlDF6zFaAdN6ANoCEdAkvVPr4WUKXV9lChoBkdAXTM5NoJzDGgHTegDaAhHQJL4XdXT3Ix1fZQoaAZHQEutHGS6lLxoB0u6aAhHQJL6zsE7nxJ1fZQoaAZHQGMHSfthNM5oB03oA2gIR0CS//b6guh9dX2UKGgGR0Bji2hkAggYaAdN6ANoCEdAkwExFqi48XV9lChoBkdAUJUpLEk0JmgHS+NoCEdAkwIokRjBmHV9lChoBkdAZuP1uivgWWgHTegDaAhHQJMD0OYplSV1fZQoaAZHQF8oAIY3vQZoB03oA2gIR0CTBaI4VARkdX2UKGgGR0BnXXmmtQsPaAdN6ANoCEdAkwslM7EHdHV9lChoBkdAZac1c+qzaGgHTegDaAhHQJMMAjrzGxV1fZQoaAZHQGZf+JP69ChoB03oA2gIR0CTD7CWu5jIdX2UKGgGR0BnW7pLVWjoaAdN6ANoCEdAkw/mIbfgrHV9lChoBkdAYw4ZydWhiGgHTegDaAhHQJMRa7rcCYF1fZQoaAZHQGJ+3RgJC0FoB03oA2gIR0CTFdgWJrLydX2UKGgGR0Bgfv0mMOwxaAdN6ANoCEdAkxZq3EyckXV9lChoBkdAaGxbTtsvZmgHTegDaAhHQJMY8LMLWqd1fZQoaAZHQGLJ34CZF5RoB03oA2gIR0CTGXvxH5JsdX2UKGgGR0BdL5PqLS/kaAdN6ANoCEdAkxwIGdI5HXV9lChoBkdAYZsnO0LMLWgHTegDaAhHQJMjQn2Iwdt1fZQoaAZHQGZ87wazeGhoB03oA2gIR0CTJPPt2LYPdX2UKGgGR0BmHLI7vG6xaAdN6ANoCEdAkyrWK2rn1XV9lChoBkdAZf5gn+hoNGgHTegDaAhHQJMrnnGKhtd1fZQoaAZHQG/MbmuDBdloB00EAmgIR0CTNO/KyOaOdX2UKGgGR0Bg7JON5t3waAdN6ANoCEdAkzZUdeY2KnV9lChoBkdAZY/rY5DJEGgHTegDaAhHQJM5pOrQw9J1fZQoaAZHQGWOyKWLP2RoB03oA2gIR0CTOjgh8pkPdX2UKGgGR0Bl0Pi704BFaAdN6ANoCEdAkz83kgfU4XV9lChoBkdARn6gGr0aqGgHTQUBaAhHQJNER24d6s11fZQoaAZHwE6kZmZmZmZoB00bAmgIR0CTRgGtZFG5dX2UKGgGR0Ap/3sXzlLfaAdL4WgIR0CTRudJaq0ddX2UKGgGR0BiKnIuGsV+aAdN6ANoCEdAk0jZHqeK9HV9lChoBkdAZI8704BFNWgHTegDaAhHQJNKqSyMUAV1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 32, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -1 +1 @@
-{"mean_reward": 276.43828163127773, "std_reward": 18.218371527590417, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-08T16:55:16.288795"}
+{"mean_reward": 250.26326390743435, "std_reward": 29.626528155801054, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-10T10:28:57.103189"}