v1

.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

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: 275.30 +/- 24.10
+      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 0x7f7ca6c28830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7ca6c288c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7ca6c28950>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7ca6c289e0>", "_build": "<function ActorCriticPolicy._build at 0x7f7ca6c28a70>", "forward": "<function ActorCriticPolicy.forward at 0x7f7ca6c28b00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7ca6c28b90>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7ca6c28c20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7ca6c28cb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7ca6c28d40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7ca6c28dd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7ca6c7d4e0>"}, "verbose": 37, "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": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651925004.1036255, "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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 460, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo_LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7b7f1c80a6e50a357d233efe35552ff7cea44fe52fe6c79aa4dc41983b8d778
+size 143986

ppo_LunarLander-v2/_stable_baselines3_version

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

ppo_LunarLander-v2/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 0x7f7ca6c28830>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7ca6c288c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7ca6c28950>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7ca6c289e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7ca6c28a70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7ca6c28b00>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7ca6c28b90>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7ca6c28c20>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7ca6c28cb0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7ca6c28d40>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7ca6c28dd0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f7ca6c7d4e0>"
+    },
+    "verbose": 37,
+    "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": 1507328,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1651925004.1036255,
+    "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": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 460,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo_LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f934992b59f60f91cb307f5d6d001dbc175bdc9d9d878fcec57c2ded6e164a8
+size 84893

ppo_LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75fb9f2de5689229c9a3e88bb834746e3fae972e62e47b3fd1aaccc92ffcdb99
+size 43201

ppo_LunarLander-v2/pytorch_variables.pth

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

ppo_LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8574a885452c188ec31d11f6f098f63b7a94bd0eb1d203eb6587ff651052cd3f
+size 199693

results.json

@@ -0,0 +1 @@
+{"mean_reward": 275.3038259168619, "std_reward": 24.104406042618713, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-07T12:52:16.021780"}