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: 242.84 +/- 14.50
+      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 0x7df6b424e710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7df6b424e7a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7df6b424e830>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7df6b424e8c0>", "_build": "<function ActorCriticPolicy._build at 0x7df6b424e950>", "forward": "<function ActorCriticPolicy.forward at 0x7df6b424e9e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7df6b424ea70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7df6b424eb00>", "_predict": "<function ActorCriticPolicy._predict at 0x7df6b424eb90>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7df6b424ec20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7df6b424ecb0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7df6b424ed40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7df6b43f8080>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1729357115117326378, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.4.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "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 sha256:904a3fd72c3dbf70adfeb967430eb4610e29aa0ec1b62db1710d402245ceae68
+size 148016

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 0x7df6b424e710>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7df6b424e7a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7df6b424e830>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7df6b424e8c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7df6b424e950>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7df6b424e9e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7df6b424ea70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7df6b424eb00>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7df6b424eb90>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7df6b424ec20>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7df6b424ecb0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7df6b424ed40>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7df6b43f8080>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1729357115117326378,
+    "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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 sha256:16fdf13ee5f1c2b19a3d40541b24826ef289f09115f61535d2d14b9a3b500f1d
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f0513e9f83843dccbf43d9c897c21c25f7c47ec5bb58309faf13fa5a94d042b
+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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.4.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.0
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 242.83901550000002, "std_reward": 14.495761763012574, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-10-19T17:24:38.814217"}