Intial commit after traning the stuff on PPO for 9e6 steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 247.01 +/- 48.64
+      value: 300.43 +/- 10.61
       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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f66ada7a790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f66ada7a820>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f66ada7a8b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f66ada7a940>", "_build": "<function ActorCriticPolicy._build at 0x7f66ada7a9d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f66ada7aa60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f66ada7aaf0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f66ada7ab80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f66ada7ac10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f66ada7aca0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f66ada7ad30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f66ada77600>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672282289632136551, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f036f1d44c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f036f1d4550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f036f1d45e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f036f1d4670>", "_build": "<function ActorCriticPolicy._build at 0x7f036f1d4700>", "forward": "<function ActorCriticPolicy.forward at 0x7f036f1d4790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f036f1d4820>", "_predict": "<function ActorCriticPolicy._predict at 0x7f036f1d48b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f036f1d4940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f036f1d49d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f036f1d4a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f036f1ccf60>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 9011200, "_total_timesteps": 9000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672304620844275090, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0012444444444443814, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2200, "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": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo_LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bd518158b7fa7726bd6982fb90083e5b1982e7e9c21fe60419990e74ecb48756
-size 147220
+oid sha256:c8d2f544fb08ae5fee8eb94f52c60efa99b00fa447052f9fb491d21be5930166
+size 146579

ppo_LunarLander-v2/data

@@ -4,19 +4,19 @@
         ":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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f66ada7a790>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f66ada7a820>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f66ada7a8b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f66ada7a940>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f66ada7a9d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f66ada7aa60>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f66ada7aaf0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f66ada7ab80>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f66ada7ac10>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f66ada7aca0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f66ada7ad30>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f036f1d44c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f036f1d4550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f036f1d45e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f036f1d4670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f036f1d4700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f036f1d4790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f036f1d4820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f036f1d48b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f036f1d4940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f036f1d49d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f036f1d4a60>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f66ada77600>"
+        "_abc_impl": "<_abc_data object at 0x7f036f1ccf60>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,12 +42,12 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 1015808,
-    "_total_timesteps": 1000000.0,
+    "num_timesteps": 9011200,
+    "_total_timesteps": 9000000.0,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1672282289632136551,
+    "start_time": 1672304620844275090,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAM3eeDzhMIS6f/DAN5GnqDKiIqk6pgThtgAAgD8AAIA/M1RIvfagILrBJwc2fi0KMT3FKDsu2CC1AACAPwAAgD8z5rm8w6k0upQoqLYvJTOykWGSO5adxjUAAIA/AACAP4BUoz2fDgg/sh4ovlxoK7/Z1bc9/WIpvgAAAAAAAAAAWumYvt8MSz9rfnG9VpEkv8APzb4uP3y7AAAAAAAAAAAzvde8UsqSPtDqqbxBPPK+DN1Yvdo8SrwAAAAAAAAAAGZnlrxDpSu8e7N+PVZGozwVhSm99twDvAAAgD8AAIA/wGqwPbdMND+GVck9UudCv5T2UD4g36g6AAAAAAAAAAAA4Pi6pH9zP1o1jbta1jW/rLsQPcRxgzwAAAAAAAAAAEBwP76aQWg/Xhb9vtvILL+457S+yUC6vgAAAAAAAAAAwG+ovTr90T7UzYw7FDEDv6XnNL5pugk9AAAAAAAAAADzAhe+jOuzPuyKHD5Spvq+F2dNvitwDz4AAAAAAAAAABqBSL04M5I/W2pSvoyiSr+BHDO997QIvgAAAAAAAAAAc3zNvcfshj+2emG+3RpTv31Vir2sAxm+AAAAAAAAAACAZIk9SAeVuiMF0DpRzb41Vr7Rumje8LkAAIA/AACAP42Tlb1Do9A+BGnDPVtUDb87P++93pYDPgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -66,16 +66,16 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": -0.0012444444444443814,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 248,
+    "_n_updates": 2200,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,

ppo_LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:963c7c8fe9c9bbec5d65bb54bf2cde21005e1fa6fc94a4abbfcfbf2059ab7517
-size 87929
+oid sha256:6700bf3f7b305079d3b93ac1598a5235c4bce34a668a5921ecbfee80119b064d
+size 87545

ppo_LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:70dcd598f42faf114c5ba1d1ec44340ade0b20b31a1b1d583057c1b4592b8aaa
-size 43201
+oid sha256:c6a8e32e4fe1d135e2cc9ac93044f99203b718b5aa08c6c0b0633b7ca9c7b07e
+size 43073

ppo_LunarLander-v2/system_info.txt

@@ -2,6 +2,6 @@ OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
 Python: 3.8.16
 Stable-Baselines3: 1.6.2
 PyTorch: 1.13.0+cu116
-GPU Enabled: True
+GPU Enabled: False
 Numpy: 1.21.6
 Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 247.0143291326661, "std_reward": 48.64477142853981, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-29T03:30:33.991534"}
+{"mean_reward": 300.4308471157418, "std_reward": 10.612499711530152, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-29T12:45:40.208196"}