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: -90.14 +/- 13.68
+      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 0x791ba71c6a70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x791ba71c6b00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x791ba71c6b90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x791ba71c6c20>", "_build": "<function ActorCriticPolicy._build at 0x791ba71c6cb0>", "forward": "<function ActorCriticPolicy.forward at 0x791ba71c6d40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x791ba71c6dd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x791ba71c6e60>", "_predict": "<function ActorCriticPolicy._predict at 0x791ba71c6ef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x791ba71c6f80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x791ba71c7010>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x791ba71c70a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x791ba7247c40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 16384, "_total_timesteps": 10000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1735199831956589019, "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": -0.6384000000000001, "_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": 24, "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:": "gAWVrQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUaACMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFowEZnVuY5SMDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+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-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": "2.5.1+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 sha256:6ac0049839c50de0890de6b40504a513615b448c755b4ba7e2e5ca6417ae1e3b
+size 147881

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 0x791ba71c6a70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x791ba71c6b00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x791ba71c6b90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x791ba71c6c20>",
+        "_build": "<function ActorCriticPolicy._build at 0x791ba71c6cb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x791ba71c6d40>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x791ba71c6dd0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x791ba71c6e60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x791ba71c6ef0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x791ba71c6f80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x791ba71c7010>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x791ba71c70a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x791ba7247c40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 16384,
+    "_total_timesteps": 10000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1735199831956589019,
+    "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": -0.6384000000000001,
+    "_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": 24,
+    "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 sha256:6dd81acd4bf9a72d18d72512903fe49d3d6b05f6f619368c7386ed136f161b97
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ce4dd2d9f137f65dba759e48ae208f846a8e50d45603e067f25f0622b3de53c
+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: 2.5.1+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": -90.13560512312942, "std_reward": 13.679292806947036, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-12-26T08:09:38.434839"}