Upload PPO LunarLander-v2 trained agent

LunarLander-PPO.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:384c3e37070ca00337a9f13dbf2a9957e43dd0ced9b882f53c502245421f5b47
+size 146870

LunarLander-PPO/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

LunarLander-PPO/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 0x7b8390237eb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b8390237f40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b8390240040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b83902400d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b8390240160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b83902401f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b8390240280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b8390240310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b83902403a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b8390240430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b83902404c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b8390240550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b83902470c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 500736,
+    "_total_timesteps": 500000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1719941881221718028,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGDexr6JyCA/wPCJvfyXg76wII27ctYLvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178,
+    "_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": 1956,
+    "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": 1,
+    "n_steps": 1024,
+    "gamma": 0.985,
+    "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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"
+    }
+}

LunarLander-PPO/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d9921bef8a0808e91581751fd830d2726c591c3612af501f4b83a057442b996
+size 87978

LunarLander-PPO/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0020d1f6b4f4e357c8a9214f9b93f5cd0a1f0704c763d43dafe43c7e33f1705
+size 43634

LunarLander-PPO/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

LunarLander-PPO/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.133+-x86_64-with-glibc2.31 # 1 SMP Tue Dec 19 13:14:11 UTC 2023
+- Python: 3.10.13
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.2+cpu
+- GPU Enabled: False
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.26.2

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: 159.81 +/- 108.68
+      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 0x7b8390237eb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b8390237f40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b8390240040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b83902400d0>", "_build": "<function ActorCriticPolicy._build at 0x7b8390240160>", "forward": "<function ActorCriticPolicy.forward at 0x7b83902401f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b8390240280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b8390240310>", "_predict": "<function ActorCriticPolicy._predict at 0x7b83902403a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b8390240430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b83902404c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b8390240550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b83902470c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 500736, "_total_timesteps": 500000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1719941881221718028, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGDexr6JyCA/wPCJvfyXg76wII27ctYLvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178, "_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": 1956, "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": 1, "n_steps": 1024, "gamma": 0.985, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS9vcHQvY29uZGEvbGliL3B5dGhvbjMuMTAvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvb3B0L2NvbmRhL2xpYi9weXRob24zLjEwL3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.133+-x86_64-with-glibc2.31 # 1 SMP Tue Dec 19 13:14:11 UTC 2023", "Python": "3.10.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.2+cpu", "GPU Enabled": "False", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.26.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 159.8109805, "std_reward": 108.67949245118905, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-07-02T18:13:44.004984"}