first RL model

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: Proximal Policy Optimization
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 260.68 +/- 16.65
+      name: mean_reward
+      verified: false
+---
+
+# **Proximal Policy Optimization** Agent playing **LunarLander-v2**
+This is a trained model of a **Proximal Policy Optimization** 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 0x7a37791b31c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a37791b3250>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a37791b32e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a37791b3370>", "_build": "<function ActorCriticPolicy._build at 0x7a37791b3400>", "forward": "<function ActorCriticPolicy.forward at 0x7a37791b3490>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a37791b3520>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a37791b35b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7a37791b3640>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a37791b36d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a37791b3760>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a37791b37f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a37815323c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713371927769915207, "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": 380, "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": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+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 sha256:44481440d1585af5e9242ee2f249b217bfd14b744368fad0480ff0de9eb02f13
+size 147963

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 0x7a37791b31c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a37791b3250>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a37791b32e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a37791b3370>",
+        "_build": "<function ActorCriticPolicy._build at 0x7a37791b3400>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7a37791b3490>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a37791b3520>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a37791b35b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7a37791b3640>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a37791b36d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a37791b3760>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a37791b37f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7a37815323c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1713371927769915207,
+    "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": 380,
+    "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": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "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+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95879c5aba4d934e4ffd175ddd96ef0051a3ef8a47720ff2a1d045eed3887dfb
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72330403edfe15924c371b52df6791c1836642d1cc6437c704d7f599010a5ee4
+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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+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": 260.67725290000004, "std_reward": 16.64776943629402, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-17T17:41:47.186016"}