default settings from course

LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ea5e9e35b2b51be6e63b26af5e20bfdec22fdff20ee16e9f706972dd8489e6b
+size 148306

LunarLander-v2/_stable_baselines3_version

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

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 0x7d86fe3579a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d86fe357a30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d86fe357ac0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d86fe357b50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d86fe357be0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d86fe357c70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d86fe357d00>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d86fe357d90>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d86fe357e20>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d86fe357eb0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d86fe357f40>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d86fe360040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d86fe35c680>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1712172227455629267,
+    "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": 284,
+    "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:": "gAWVowEAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlIwUbnVtcHkucmFuZG9tLl9waWNrbGWUjBBfX2dlbmVyYXRvcl9jdG9ylJOUjAVQQ0c2NJRoG4wUX19iaXRfZ2VuZXJhdG9yX2N0b3KUk5SGlFKUfZQojA1iaXRfZ2VuZXJhdG9ylIwFUENHNjSUjAVzdGF0ZZR9lChoJooRo5X6A9aWX3XBNKgjcyy7yQCMA2luY5SKERElAEIVN02qEAsVq67hNs4AdYwKaGFzX3VpbnQzMpRLAYwIdWludGVnZXKUShRIqS51YnViLg==",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "Generator(PCG64)"
+    },
+    "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc74e3862bc5db2897429c495d02689f52027ce4278c55404eb7e534a04c6e49
+size 88362

LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f985dbde22245439ea00400d84f86a3f254b5d4e41213de2ac4ccade227e17cd
+size 43762

LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

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

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: 271.17 +/- 18.30
+      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 0x7d86fe3579a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d86fe357a30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d86fe357ac0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d86fe357b50>", "_build": "<function ActorCriticPolicy._build at 0x7d86fe357be0>", "forward": "<function ActorCriticPolicy.forward at 0x7d86fe357c70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d86fe357d00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d86fe357d90>", "_predict": "<function ActorCriticPolicy._predict at 0x7d86fe357e20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d86fe357eb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d86fe357f40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d86fe360040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d86fe35c680>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712172227455629267, "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": 284, "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:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "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": "2.2.1+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": 271.1656991, "std_reward": 18.29556237187033, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-03T20:03:22.870090"}