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: 271.84 +/- 15.49
+      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 0x79b5f387b250>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79b5f387b2e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79b5f387b370>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79b5f387b400>", "_build": "<function ActorCriticPolicy._build at 0x79b5f387b490>", "forward": "<function ActorCriticPolicy.forward at 0x79b5f387b520>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79b5f387b5b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79b5f387b640>", "_predict": "<function ActorCriticPolicy._predict at 0x79b5f387b6d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79b5f387b760>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79b5f387b7f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79b5f387b880>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79b5f382b080>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1703771048014453595, "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "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:a346f4adc3f44371714513bf90bd3eb3dc5a7620e6d03bf2613a78ee6f381337
+size 148036

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 0x79b5f387b250>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79b5f387b2e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79b5f387b370>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79b5f387b400>",
+        "_build": "<function ActorCriticPolicy._build at 0x79b5f387b490>",
+        "forward": "<function ActorCriticPolicy.forward at 0x79b5f387b520>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x79b5f387b5b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79b5f387b640>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x79b5f387b6d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79b5f387b760>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79b5f387b7f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x79b5f387b880>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x79b5f382b080>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1703771048014453595,
+    "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 sha256:30eb773139f30b07bf565839237a81391acb9e3e3547109e9442e8186569a114
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e4a30e9332477408ce1437140366b1aa4824dab77304308e291a9ea01978e07
+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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu121
+- 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": 271.8432193661112, "std_reward": 15.488815877706223, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-28T14:12:32.860269"}