Committed the Lunar Lander v2 - chapter 2

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 244.81 +/- 18.61
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+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)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +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 0x793d6aca93f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793d6aca9480>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x793d6aca9510>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x793d6aca95a0>", "_build": "<function ActorCriticPolicy._build at 0x793d6aca9630>", "forward": "<function ActorCriticPolicy.forward at 0x793d6aca96c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x793d6aca9750>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x793d6aca97e0>", "_predict": "<function ActorCriticPolicy._predict at 0x793d6aca9870>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x793d6aca9900>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x793d6aca9990>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x793d6aca9a20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x793d85de4400>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701190770312197108, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_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": 16, "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-5.15.120+-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"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69440af53c851ce5d4534c6280199f558bda1f3e97278632d489b76403c33dd7
+size 148054

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 0x793d6aca93f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793d6aca9480>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x793d6aca9510>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x793d6aca95a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x793d6aca9630>",
+        "forward": "<function ActorCriticPolicy.forward at 0x793d6aca96c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x793d6aca9750>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x793d6aca97e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x793d6aca9870>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x793d6aca9900>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x793d6aca9990>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x793d6aca9a20>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x793d85de4400>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1701190770312197108,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_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": 16,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f22cfce4807249b3b6103edef9114a1679175b1466a8c5c31aac33033ab52c9e
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0463d5bb5448c9df0bd293eaf9f54370376f6a8420006da56b099661adb64587
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- 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

@@ -0,0 +1 @@
+{"mean_reward": 244.81195340000005, "std_reward": 18.60977337889022, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-28T17:42:03.530175"}