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: 173.17 +/- 30.58
+      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 0x7f5e84186170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5e84186200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5e84186290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5e84186320>", "_build": "<function ActorCriticPolicy._build at 0x7f5e841863b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f5e84186440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5e841864d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5e84186560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5e841865f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5e84186680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5e84186710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5e841cf960>"}, "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": 1652377609.7030683, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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 sha256:7a156e185ea0fb32f0825a7c2be156b0c4155785f0d9370a1c9b7993161cca80
+size 144048

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 0x7f5e84186170>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5e84186200>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5e84186290>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5e84186320>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5e841863b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5e84186440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5e841864d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5e84186560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5e841865f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5e84186680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5e84186710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5e841cf960>"
+    },
+    "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": 1652377609.7030683,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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:e671570723b164b65f319f0bc8b99abd2d1bbe782add501b5aac9c1c0d559a20
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d867dd3f004d700ce33006d2be32035d3027d80553ebc6a07ea56306db5eaf7b
+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 sha256:393348a39dccc99f5430d81426ff58d30522fbf95a101172e1c19255cfa40708
+size 264585

results.json

@@ -0,0 +1 @@
+{"mean_reward": 173.165323740431, "std_reward": 30.577218836771262, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-12T18:00:23.019948"}