1st try at PPO LunarLander following course code

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 202.03 +/- 46.46
+      value: 246.24 +/- 22.68
       name: mean_reward
     task:
       type: reinforcement-learning

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 0x7f7412e315e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7412e31670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7412e31700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7412e31790>", "_build": "<function ActorCriticPolicy._build at 0x7f7412e31820>", "forward": "<function ActorCriticPolicy.forward at 0x7f7412e318b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7412e31940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7412e319d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7412e31a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7412e31af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7412e31b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7412e336c0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652833987.4479814, "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": 124, "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-5.4.0-109-generic-x86_64-with-glibc2.27 #123~18.04.1-Ubuntu SMP Fri Apr 8 09:48:52 UTC 2022", "Python": "3.9.12", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.21.5", "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 0x7fb8588f84c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb8588f8550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb8588f85e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb8588f8670>", "_build": "<function ActorCriticPolicy._build at 0x7fb8588f8700>", "forward": "<function ActorCriticPolicy.forward at 0x7fb8588f8790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb8588f8820>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb8588f88b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb8588f8940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb8588f89d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb8588f8a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fb858a02a40>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652837204.5343592, "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": 124, "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-5.4.0-109-generic-x86_64-with-glibc2.27 #123~18.04.1-Ubuntu SMP Fri Apr 8 09:48:52 UTC 2022", "Python": "3.9.12", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.22.3", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c5a0c3674706e02d5c06ba444af8053dc5ab860f632a0c3c64f2d136f34368c2
-size 144174
+oid sha256:7ccc6941261da79993f11a2edd59098494df3221f40223e5d6f53c653584e139
+size 144182

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 0x7f7412e315e0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7412e31670>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7412e31700>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7412e31790>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f7412e31820>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f7412e318b0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7412e31940>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f7412e319d0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7412e31a60>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7412e31af0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7412e31b80>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fb8588f84c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb8588f8550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb8588f85e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb8588f8670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb8588f8700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb8588f8790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb8588f8820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb8588f88b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb8588f8940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb8588f89d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb8588f8a60>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f7412e336c0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7fb858a02a40>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -47,7 +47,7 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1652833987.4479814,
+    "start_time": 1652837204.5343592,
     "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:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -69,7 +69,7 @@
     "_current_progress_remaining": -0.015808000000000044,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:015900f134684dcc742b7ebf144abe6222345b1fc06cff2f8bca6c55d02225aa
+oid sha256:9da269d25cf3a42e72d4a391f9c1526627788546f51f906afaf375a9b10915a6
 size 84829

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1db4ac98a83cd40439129fc28b6fb30ce6626d2fd42aa68407aeabf6f46ba92a
+oid sha256:e03082c8866a42a6664cdc014ac75f2800be11fdbae4941f9cc0a989da7800f4
 size 43201

ppo-LunarLander-v2/system_info.txt

@@ -3,5 +3,5 @@ Python: 3.9.12
 Stable-Baselines3: 1.5.0
 PyTorch: 1.11.0
 GPU Enabled: True
-Numpy: 1.21.5
+Numpy: 1.22.3
 Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 202.03076065343618, "std_reward": 46.4560216037407, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-17T21:16:14.524203"}
+{"mean_reward": 246.24176198134492, "std_reward": 22.675651928636633, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-17T21:35:05.777458"}