272 +/- 11.14, improved PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 199.81 +/- 59.34
+      value: 261.74 +/- 18.72
       name: mean_reward
       verified: false
 ---
@@ -26,35 +26,12 @@ This is a trained model of a **PPO** agent playing **LunarLander-v2**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)
-```python
-!pip install shimmy
-
-from huggingface_sb3 import load_from_hub
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.evaluation import evaluate_policy
-
-repo_id = "jaymanvirk/ppo-LunarLander-v2"  # The repo_id
-filename = "ppo-LunarLander-v2.zip"  # The model filename.zip
+TODO: Add your code
 
-'''
-When the model was trained on Python 3.8 the pickle protocol is 5
-But Python 3.6, 3.7 use protocol 4
-In order to get compatibility we need to:
-1. Install pickle5 (we done it at the beginning of the colab)
-2. Create a custom empty object we pass as parameter to PPO.load()
 
-'''
-custom_objects = {
-    "learning_rate": 0.0,
-    "lr_schedule": lambda _: 0.0,
-    "clip_range": lambda _: 0.0,
-}
-
-checkpoint = load_from_hub(repo_id, filename)
-model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
 
-eval_env = Monitor(gym.make("LunarLander-v2"))
-mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
-print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
-```
+...
+```

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 0x7e8bec62a290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e8bec62a320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e8bec62a3b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e8bec62a440>", "_build": "<function ActorCriticPolicy._build at 0x7e8bec62a4d0>", "forward": "<function ActorCriticPolicy.forward at 0x7e8bec62a560>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e8bec62a5f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e8bec62a680>", "_predict": "<function ActorCriticPolicy._predict at 0x7e8bec62a710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e8bec62a7a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e8bec62a830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e8bec62a8c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e8bec625d40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2000896, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691319362818658635, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAADPz3bs9Okq5ikh/ORH5HDWuB4g627KXuAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 7816, "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": 1, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.99, "ent_coef": 0.1, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "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 0x7db3fa8916c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7db3fa891750>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7db3fa8917e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7db3fa891870>", "_build": "<function ActorCriticPolicy._build at 0x7db3fa891900>", "forward": "<function ActorCriticPolicy.forward at 0x7db3fa891990>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7db3fa891a20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7db3fa891ab0>", "_predict": "<function ActorCriticPolicy._predict at 0x7db3fa891b40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7db3fa891bd0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7db3fa891c60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7db3fa891cf0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7db3fa898980>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1693252822137071692, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGBffr6beWE/nazyPa0ai77ScR69BPCTPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": 1, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6a2758590748b064b90b23d2844db4df8f1757f1147be6e6c6be63629044c92a
-size 146035
+oid sha256:688c96b1a71c76210800b3aadba667e1be4a18ebd039085a570b14490640660f
+size 146076

ppo-LunarLander-v2/data

@@ -4,34 +4,34 @@
         ":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 0x7e8bec62a290>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e8bec62a320>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e8bec62a3b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e8bec62a440>",
-        "_build": "<function ActorCriticPolicy._build at 0x7e8bec62a4d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7e8bec62a560>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e8bec62a5f0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e8bec62a680>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7e8bec62a710>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e8bec62a7a0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e8bec62a830>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e8bec62a8c0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7db3fa8916c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7db3fa891750>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7db3fa8917e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7db3fa891870>",
+        "_build": "<function ActorCriticPolicy._build at 0x7db3fa891900>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7db3fa891990>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7db3fa891a20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7db3fa891ab0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7db3fa891b40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7db3fa891bd0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7db3fa891c60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7db3fa891cf0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7e8bec625d40>"
+        "_abc_impl": "<_abc._abc_data object at 0x7db3fa898980>"
     },
     "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 2000896,
-    "_total_timesteps": 2000000,
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1691319362818658635,
+    "start_time": 1693252822137071692,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAADPz3bs9Okq5ikh/ORH5HDWuB4g627KXuAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGBffr6beWE/nazyPa0ai77ScR69BPCTPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -45,13 +45,13 @@
     "_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'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 7816,
+    "_n_updates": 3908,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
         ":serialized:": "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",
@@ -77,14 +77,14 @@
         "_np_random": null
     },
     "n_envs": 1,
-    "n_steps": 2048,
+    "n_steps": 1024,
     "gamma": 0.999,
-    "gae_lambda": 0.99,
-    "ent_coef": 0.1,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,
-    "n_epochs": 8,
+    "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
         ":serialized:": "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"

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fc94fd14f5b7a80233de77136991d774aadd3fd5b8db77c98cf1d458e1204982
+oid sha256:53e35cec6ec5ecf5fe8d9d88b356655eda7a08bbf324455749b5f294fdf409bf
 size 87929

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f81b513385670cb9a2ea00f6666e46a708cf8f2fe50848d34cb9d4aca0d701dd
+oid sha256:0e6ce390eba07ec650d8b7bd888f85a6d8fde8b6d4d255cdb43a24111588a0f7
 size 43329

ppo-LunarLander-v2/system_info.txt

@@ -3,7 +3,7 @@
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.0.1+cu118
 - GPU Enabled: True
-- Numpy: 1.22.4
+- Numpy: 1.23.5
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
 - OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 199.8053116, "std_reward": 59.34347973999889, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-06T12:24:24.062776"}
+{"mean_reward": 261.73759600000005, "std_reward": 18.7168759778072, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-28T20:33:01.827016"}