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: 38.59 +/- 117.90
+      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 0x7f4a2a517d40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4a2a517dd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4a2a517e60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4a2a517ef0>", "_build": "<function ActorCriticPolicy._build at 0x7f4a2a517f80>", "forward": "<function ActorCriticPolicy.forward at 0x7f4a2a51f050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4a2a51f0e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4a2a51f170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4a2a51f200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4a2a51f290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4a2a51f320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4a2a5659f0>"}, "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": 1651766294.6703784, "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 sha256:649ca075e58611bd89aaedbebf6311e5c12859e7c6a412b21d25c9c82cd68fcb
+size 144040

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 0x7f4a2a517d40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4a2a517dd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4a2a517e60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4a2a517ef0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4a2a517f80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4a2a51f050>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4a2a51f0e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4a2a51f170>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4a2a51f200>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4a2a51f290>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4a2a51f320>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f4a2a5659f0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1651766294.6703784,
+    "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 sha256:8b414c1a8fbb9d1959a133a7051c960ce0c0d20306028853cfd7f53509d1e735
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7edea3d3586b1248c0173d7abfb7b6be2117036fd931d7604962346e8d763c74
+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 sha256:6531ef5e2f0adb1184a230902ff3049927b1e65e96efc8600ac101901d1c8658
+size 255333

results.json

@@ -0,0 +1 @@
+{"mean_reward": 38.59316637754832, "std_reward": 117.89869800090221, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T16:14:32.385922"}