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: 264.41 +/- 9.63
+      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 0x7e03386ad120>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e03386ad1b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e03386ad240>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e03386ad2d0>", "_build": "<function ActorCriticPolicy._build at 0x7e03386ad360>", "forward": "<function ActorCriticPolicy.forward at 0x7e03386ad3f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e03386ad480>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e03386ad510>", "_predict": "<function ActorCriticPolicy._predict at 0x7e03386ad5a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e03386ad630>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e03386ad6c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e03386ad750>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e033864e800>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700919667999307691, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGbtt72hXgc/zzQrPeD0V77yYci85kSpPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": 1, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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 sha256:99cdf4ef2d100cc99c160fa602d458a33391bef54144b2715ea4b911722f1e4f
+size 147400

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 0x7e03386ad120>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e03386ad1b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e03386ad240>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e03386ad2d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7e03386ad360>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7e03386ad3f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e03386ad480>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e03386ad510>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7e03386ad5a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e03386ad630>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e03386ad6c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e03386ad750>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e033864e800>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1700919667999307691,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGbtt72hXgc/zzQrPeD0V77yYci85kSpPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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": 1,
+    "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 sha256:590b623441861fba05c9212dccb2c9798ee2f1d916267b252054a18ab938c9e7
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d142d89f989c80619f75d9070a8ccbcb0d0c5f3fdbdfb2a59b259d5080a0d21c
+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": 264.4077093, "std_reward": 9.628495875033122, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-25T14:24:44.896479"}