initial commit, uploading fully trained PPO agent

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: 266.45 +/- 21.43
+      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 0x7fb663b5e290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb663b5e320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb663b5e3b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb663b5e440>", "_build": "<function ActorCriticPolicy._build at 0x7fb663b5e4d0>", "forward": "<function ActorCriticPolicy.forward at 0x7fb663b5e560>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb663b5e5f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb663b5e680>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb663b5e710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb663b5e7a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb663b5e830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb663b5e8c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fb663b5a200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682953222975986041, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAQAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 280, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVpQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoC0sIhZSMAUOUdJRSlIwEaGlnaJRoEyiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoC0sIhZRoFnSUUpSMDWJvdW5kZWRfYmVsb3eUaBMolggAAAAAAAAAAAAAAAAAAACUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYIAAAAAAAAAAAAAAAAAAAAlGgiSwiFlGgWdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 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+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

irow_lunar_lander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:318b881ab97744511e41fd0110d038cea14e23caa77f198ff7805c8aa7981c01
+size 146573

irow_lunar_lander/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

irow_lunar_lander/data

@@ -0,0 +1,93 @@
+{
+    "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 0x7fb663b5e290>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb663b5e320>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb663b5e3b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb663b5e440>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb663b5e4d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb663b5e560>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb663b5e5f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb663b5e680>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb663b5e710>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb663b5e7a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb663b5e830>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb663b5e8c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fb663b5a200>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682953222975986041,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAQAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 280,
+    "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": 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
+}

irow_lunar_lander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8cb7b4098de352e577a43fa976a9743c8502e3532916263176434c8997f6c4b
+size 88057

irow_lunar_lander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8df0a2398877591a01d4dacb5ad88b5fc252894ea20e87ff899ec968ee63da6
+size 43329

irow_lunar_lander/pytorch_variables.pth

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

irow_lunar_lander/system_info.txt

@@ -0,0 +1,7 @@
+- 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: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 266.4474000993661, "std_reward": 21.42938736276062, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-01T15:30:27.939742"}