Upload PPO LunarLander-v2 trained agent

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: 259.28 +/- 19.94
+      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 0x7b61a88bd090>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b61a88bd120>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b61a88bd1b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b61a88bd240>", "_build": "<function ActorCriticPolicy._build at 0x7b61a88bd2d0>", "forward": "<function ActorCriticPolicy.forward at 0x7b61a88bd360>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b61a88bd3f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b61a88bd480>", "_predict": "<function ActorCriticPolicy._predict at 0x7b61a88bd510>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b61a88bd5a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b61a88bd630>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b61a88bd6c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b61c3225500>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700533382903469613, "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": 252, "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 sha256:9b9ebe438adce4f99a2778211def1e708fa38167b9c2b313d32b77c54455ae1a
+size 148010

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 0x7b61a88bd090>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b61a88bd120>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b61a88bd1b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b61a88bd240>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b61a88bd2d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b61a88bd360>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b61a88bd3f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b61a88bd480>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b61a88bd510>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b61a88bd5a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b61a88bd630>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b61a88bd6c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b61c3225500>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1700533382903469613,
+    "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": 252,
+    "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:e64dc8c96321c81d64534b47052d69e1db41606f80915b2c080fb2007e5feae1
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8e1bc8f10ac831080d3b6c17d419b6338248d3b053ba464724ff4b29ec70a4e
+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": 259.27873135984635, "std_reward": 19.935729392215325, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-21T03:23:16.985515"}