First Training of RL course

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: 246.09 +/- 32.08
+      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 0x783d5473cee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x783d5473cf70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x783d5473d000>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x783d5473d090>", "_build": "<function ActorCriticPolicy._build at 0x783d5473d120>", "forward": "<function ActorCriticPolicy.forward at 0x783d5473d1b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x783d5473d240>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x783d5473d2d0>", "_predict": "<function ActorCriticPolicy._predict at 0x783d5473d360>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x783d5473d3f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x783d5473d480>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x783d5473d510>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x783d546ddb00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1710309443081384619, "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:": "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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"}, "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": "2.1.0+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 sha256:154a17144b42ab68957ad1b7d8704eeb293e16d9c66120ca40e28056e3f4965c
+size 148076

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 0x783d5473cee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x783d5473cf70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x783d5473d000>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x783d5473d090>",
+        "_build": "<function ActorCriticPolicy._build at 0x783d5473d120>",
+        "forward": "<function ActorCriticPolicy.forward at 0x783d5473d1b0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x783d5473d240>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x783d5473d2d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x783d5473d360>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x783d5473d3f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x783d5473d480>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x783d5473d510>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x783d546ddb00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1710309443081384619,
+    "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:": "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:": "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:c2bbf0f67fb28a8e6aad24e343c70987ed8caeed567d06c9e3b88b1f99800b24
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49d99c23e797224a40cf56a9c48febdb9678394303fb7177ffb3594bbc545336
+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: 2.1.0+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": 246.0908417, "std_reward": 32.07802977485185, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-13T07:43:16.021698"}