Init commit

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

PPO-MLP.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49e0fb7027893f30579c968ebc35ddffcf1b42220873dbca1cc9cf74ff925153
+size 143932

PPO-MLP/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

PPO-MLP/data

@@ -0,0 +1,94 @@
+{
+    "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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x17a054040>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x17a0540d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x17a054160>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x17a0541f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x17a054280>",
+        "forward": "<function ActorCriticPolicy.forward at 0x17a054310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x17a0543a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x17a054430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x17a0544c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x17a054550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x17a0545e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x17976c480>"
+    },
+    "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1651742929.270856,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVIwMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjHkvVXNlcnMvcGF0cmljay8ucHllbnYvdmVyc2lvbnMvMy45LjExL2VudnMvaGYtZGVlcC1ybC1jbGFzcy9saWIvcHl0aG9uMy45L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIx5L1VzZXJzL3BhdHJpY2svLnB5ZW52L3ZlcnNpb25zLzMuOS4xMS9lbnZzL2hmLWRlZXAtcmwtY2xhc3MvbGliL3B5dGhvbjMuOS9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGggfZR9lChoF2gOjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoGIwHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 124,
+    "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
+}

PPO-MLP/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afab1fefb2c0c22ebfdabcb8ba559c9991c3fc8ae3818ce439ec4860c17acf78
+size 84573

PPO-MLP/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd2a207f8ac48130bd2c88c96f2684285ee61de61e27fc37ae7c02cdef5e017b
+size 43073

PPO-MLP/pytorch_variables.pth

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

PPO-MLP/system_info.txt

@@ -0,0 +1,7 @@
+OS: macOS-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:46:32 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T6000
+Python: 3.9.11
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0
+GPU Enabled: False
+Numpy: 1.22.3
+Gym: 0.21.0

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 217.36 +/- 19.27
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **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
+  

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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x17a054040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x17a0540d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x17a054160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x17a0541f0>", "_build": "<function ActorCriticPolicy._build at 0x17a054280>", "forward": "<function ActorCriticPolicy.forward at 0x17a054310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x17a0543a0>", "_predict": "<function ActorCriticPolicy._predict at 0x17a054430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x17a0544c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x17a054550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x17a0545e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x17976c480>"}, "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651742929.270856, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "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-12.3.1-arm64-arm-64bit Darwin Kernel Version 21.4.0: Fri Mar 18 00:46:32 PDT 2022; root:xnu-8020.101.4~15/RELEASE_ARM64_T6000", "Python": "3.9.11", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "False", "Numpy": "1.22.3", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ffaabf3835d3b910cb2ec6065b04b9cbf08582f9b295a6f7c6c100b74ea773d0
+size 406591

results.json

@@ -0,0 +1 @@
+{"mean_reward": 217.35990046818014, "std_reward": 19.268675571519836, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T13:20:11.329093"}