Upload PPO LunarLander-v2 trained agent

.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: 152.07 +/- 77.48
+      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 0x7f336feada70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f336feadb00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f336feadb90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f336feadc20>", "_build": "<function ActorCriticPolicy._build at 0x7f336feadcb0>", "forward": "<function ActorCriticPolicy.forward at 0x7f336feadd40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f336feaddd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f336feade60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f336feadef0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f336feadf80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f336feb1050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f336fefa930>"}, "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": 1652389265.4846802, "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": "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:b19d3f942aae2d294544cbd33d38765b6757adf0f4927e2617240d8f927acf6c
+size 144024

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 0x7f336feada70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f336feadb00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f336feadb90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f336feadc20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f336feadcb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f336feadd40>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f336feaddd0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f336feade60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f336feadef0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f336feadf80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f336feb1050>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f336fefa930>"
+    },
+    "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": 1652389265.4846802,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee98a7bf8afbac12b85002af31f18e3df722c5136aa97b1fb8b9a92f0dcebf9e
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edeb814df6398659044fa1bf8b4bc2b15bcdc9c5a3f0fb83c2e5bcf349169c64
+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 sha256:1bd5a8443ef34e47a81377a408c013b16261658b4b8244123429c364d1b1220b
+size 247887

results.json

@@ -0,0 +1 @@
+{"mean_reward": 152.06844467444904, "std_reward": 77.48262718915777, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-12T21:33:47.508405"}