Iteration 4.2

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: 287.92 +/- 23.96
+      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 0x7fc842d88160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc842d881f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc842d88280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc842d88310>", "_build": "<function ActorCriticPolicy._build at 0x7fc842d883a0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc842d88430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc842d884c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc842d88550>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc842d885e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc842d88670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc842d88700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc842d88790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc842d86c80>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 128, "num_timesteps": 10092544, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674919427638141096, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWV8wAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYkuAhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.009254400000000107, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 308, "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, "system_info": {"OS": "macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:47:26 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T8101", "Python": "3.9.13", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0.dev20230126", "GPU Enabled": "False", "Numpy": "1.23.5", "Gym": "0.21.0"}}

eldraco_lunarlanderv2_v4.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f45f506f9b26381f58c68ec199c14a6cd1dff1de531273c4c2e978244c9994d4
+size 151900

eldraco_lunarlanderv2_v4/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

eldraco_lunarlanderv2_v4/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7fc842d88160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc842d881f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc842d88280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc842d88310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc842d883a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc842d88430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc842d884c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc842d88550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc842d885e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc842d88670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc842d88700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc842d88790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fc842d86c80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 128,
+    "num_timesteps": 10092544,
+    "_total_timesteps": 10000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1674919427638141096,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWV8QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMXy9Vc2Vycy9zZWJhcy9taW5pY29uZGEzL2VudnMvYW5vbS9saWIvcHl0aG9uMy45L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgkMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxfL1VzZXJzL3NlYmFzL21pbmljb25kYTMvZW52cy9hbm9tL2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWV8wAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYkuAhZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.009254400000000107,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 308,
+    "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
+}

eldraco_lunarlanderv2_v4/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6d0ff930d4bd087ab7daf2093126cf37281b9a70ab4f684cb44411a540c0761
+size 87545

eldraco_lunarlanderv2_v4/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c81b19f6ff9da5778b0df239c6c47e2d5c8b5ad124d8b849435d77793147f2c9
+size 43265

eldraco_lunarlanderv2_v4/pytorch_variables.pth

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

eldraco_lunarlanderv2_v4/system_info.txt

@@ -0,0 +1,7 @@
+- OS: macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:47:26 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T8101
+- Python: 3.9.13
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0.dev20230126
+- GPU Enabled: False
+- Numpy: 1.23.5
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 287.918651, "std_reward": 23.956931828240172, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-29T11:35:08.621160"}