initial

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: ppo-mlp
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 241.98 +/- 21.78
+      name: mean_reward
+      verified: false
+---
+
+# **ppo-mlp** Agent playing **LunarLander-v2**
+This is a trained model of a **ppo-mlp** 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 0x7eac49f2c3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eac49f2c430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eac49f2c4c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eac49f2c550>", "_build": "<function ActorCriticPolicy._build at 0x7eac49f2c5e0>", "forward": "<function ActorCriticPolicy.forward at 0x7eac49f2c670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7eac49f2c700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eac49f2c790>", "_predict": "<function ActorCriticPolicy._predict at 0x7eac49f2c820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eac49f2c8b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eac49f2c940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eac49f2c9d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7eac49f30200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1718286622764411473, "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, "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, "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:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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 Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "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 sha256:3ed1d3a482d0246fdd65f05719cc3e085d6e9b884f43b9396565844776b884fc
+size 148258

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 0x7eac49f2c3a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eac49f2c430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eac49f2c4c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eac49f2c550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7eac49f2c5e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7eac49f2c670>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7eac49f2c700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eac49f2c790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7eac49f2c820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eac49f2c8b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eac49f2c940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eac49f2c9d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7eac49f30200>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1718286622764411473,
+    "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,
+    "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,
+    "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:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37be8668f6b1b249397b47b77064bede2b0c2996e2262343f3b9b3113177eaad
+size 88490

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20d1fc0b578bf07bc41a0676b8eff6775e371924fd0f61438eabfee072acedde
+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 Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 241.9764661, "std_reward": 21.78399011380819, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-13T14:31:18.110165"}