Version1

.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_LunarLander_v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b1d5bc14714e010c26ec03044486701a5d057a71e022e7cc93004489afd0489
+size 144092

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 0x7f945b271560>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f945b2715f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f945b271680>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f945b271710>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f945b2717a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f945b271830>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f945b2718c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f945b271950>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f945b2719e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f945b271a70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f945b271b00>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f945b2b3c60>"
+    },
+    "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": 1652240996.5022101,
+    "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": 310,
+    "n_steps": 1024,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.01,
+    "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:d67b5ae44a0993467978139abab08bfa45e76ab573522b4e17e0738917603cc9
+size 84893

PPO_LunarLander_v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8437bf6aa4b10d635ee5ca3f4bf443004ec5562fe00adabb09ea4c9a0eeb3b88
+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

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: 215.32 +/- 46.32
+      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 0x7f945b271560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f945b2715f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f945b271680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f945b271710>", "_build": "<function ActorCriticPolicy._build at 0x7f945b2717a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f945b271830>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f945b2718c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f945b271950>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f945b2719e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f945b271a70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f945b271b00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f945b2b3c60>"}, "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": 1652240996.5022101, "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": 310, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.01, "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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99b50671de2b44f4b780293640212c43acfde161c4596a64c9a007694f2f831b
+size 241032

results.json

@@ -0,0 +1 @@
+{"mean_reward": 215.317499739968, "std_reward": 46.31716351422818, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-11T04:32:06.356670"}