hands-on lunar lander

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: 265.56 +/- 17.89
+      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 0x7f9697771b40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9697771bd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9697771c60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9697771cf0>", "_build": "<function ActorCriticPolicy._build at 0x7f9697771d80>", "forward": "<function ActorCriticPolicy.forward at 0x7f9697771e10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9697771ea0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9697771f30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9697771fc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9697772050>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f96977720e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9697772170>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f96977633c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683482483082688349, "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, "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": 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:": "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

model_trained.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c077bdac2c0e62ed9c4dc9f744f04e016274c3ccca03f8f32d29f7d66279d643
+size 146739

model_trained/_stable_baselines3_version

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

model_trained/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 0x7f9697771b40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9697771bd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9697771c60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9697771cf0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9697771d80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9697771e10>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9697771ea0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9697771f30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9697771fc0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9697772050>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f96977720e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9697772170>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f96977633c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1683482483082688349,
+    "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,
+    "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": 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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"
+    }
+}

model_trained/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4cf8560212f104cff256386212e4d41f1dd7886591556ee04baec12dc3f9af72
+size 87929

model_trained/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b051d5f341433fc558611dfbf2851fcd002d21c66b0cd72b4a464c11cc1eea3
+size 43329

model_trained/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

model_trained/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 265.5583736935153, "std_reward": 17.885974532230495, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-07T18:33:38.153693"}