Trained Lunar Lander-V2 Agent

README.md

@@ -1,3 +1,37 @@
----
-license: mit
----
+---
+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: 275.05 +/- 19.27
+      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 0x784bdf1ad1b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784bdf1ad240>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784bdf1ad2d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784bdf1ad360>", "_build": "<function ActorCriticPolicy._build at 0x784bdf1ad3f0>", "forward": "<function ActorCriticPolicy.forward at 0x784bdf1ad480>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x784bdf1ad510>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784bdf1ad5a0>", "_predict": "<function ActorCriticPolicy._predict at 0x784bdf1ad630>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784bdf1ad6c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784bdf1ad750>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x784bdf1ad7e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x784bdf356640>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1722718706891961368, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAAB53jxcb1S62U8huySV0LXnaBu7MCM4NQAAgD8AAIA/M2F/vcpnPz5jO9o8+lEXvgazCrxh2Ig8AAAAAAAAAADNGEQ8w/UDusI377ei6v6xFbe3u0+2DTcAAIA/AACAP+YMpD2ubZ26Wrg0u44w6ze8SXc6zi7bOQAAgD8AAIA/bUtDvp0clT/ONaK+5avjvj5FdL6ajDY9AAAAAAAAAAAAyPc7rnmIulrovTteoCA4udHmOTJy/bYAAIA/AACAP81dTb0p4Ei6tX1AO6aSGbbAHZO7eBBfugAAgD8AAIA/OkymPuDzuj7X9UE965Osvn9JXT4ic7O9AAAAAAAAAADNDKM67AHouZ81kDnZB8M0KSqCO+NwrLgAAIA/AACAP2YH4j2DMhy8R6xKvudiCrsDfX89haQQvgAAgD8AAIA/AGwsva7Pibp7PBW5faWGtNSrFDsGPCs4AACAPwAAgD9Ntxg9e8aOuq8KmboCrRw29G/+OeKmrzkAAIA/AACAPzNw5byCAEM/cN9vvR6Iub5LumG91UbxPAAAAAAAAAAAzSfCPXXw8j51GLs8SwKnvrQ9zj2uM7I7AAAAAAAAAAAAAN68e8KDunZBNTmkPyw0bnK5ujptU7gAAIA/AACAP006Br2F4+u5LeOmOYktIjO3IUO75MLFuAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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": "2.3.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "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 sha256:260045c393b4d3154973171f061dfe736d10553f7b91661bf89b0ee690ce1342
+size 148084

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 0x784bdf1ad1b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784bdf1ad240>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784bdf1ad2d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784bdf1ad360>",
+        "_build": "<function ActorCriticPolicy._build at 0x784bdf1ad3f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x784bdf1ad480>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x784bdf1ad510>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784bdf1ad5a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x784bdf1ad630>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784bdf1ad6c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784bdf1ad750>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x784bdf1ad7e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x784bdf356640>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1722718706891961368,
+    "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 sha256:312a4296b8c60b7430b0f4d61acab87c746b1c17fc02a3bdeecda9e0b8e1fe19
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6813fd7869df40c54d48fadf67ef4c99379124daddb137efaf0a2afeeaba6b6f
+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: 2.3.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 275.0456275, "std_reward": 19.26887328985738, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-08-03T21:35:04.288782"}