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: 52.06 +/- 98.79
+      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 0x7fc9f19aa160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc9f19aa1f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc9f19aa280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc9f19aa310>", "_build": "<function ActorCriticPolicy._build at 0x7fc9f19aa3a0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc9f19aa430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc9f19aa4c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc9f19aa550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc9f19aa5e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc9f19aa670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc9f19aa700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc9f19a4540>"}, "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": 32, "num_timesteps": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652214349.468962, "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:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 64, "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-5.4.0-109-generic-x86_64-with-glibc2.29 #123-Ubuntu SMP Fri Apr 8 09:10:54 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.2", "Gym": "0.21.0"}}

nkt32/PPO-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba6707d2a330616649d7fe8a0258edeee6cce160fdfadf0384a431e23cf87a9b
+size 144516

nkt32/PPO-LunarLander-v2/_stable_baselines3_version

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

nkt32/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 0x7fc9f19aa160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc9f19aa1f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc9f19aa280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc9f19aa310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc9f19aa3a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc9f19aa430>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc9f19aa4c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc9f19aa550>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc9f19aa5e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc9f19aa670>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc9f19aa700>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fc9f19a4540>"
+    },
+    "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": 32,
+    "num_timesteps": 524288,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652214349.468962,
+    "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:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.04857599999999995,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 64,
+    "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
+}

nkt32/PPO-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:579bfb7eacfc438f5affaf4e86e6a9b9d782e74c7d28b3005bb55745c9bda422
+size 84829

nkt32/PPO-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c847a15db9b747ddd6ac44c3de484e982a85d22c7684406b00df8117c9c260d
+size 43201

nkt32/PPO-LunarLander-v2/pytorch_variables.pth

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

nkt32/PPO-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.0-109-generic-x86_64-with-glibc2.29 #123-Ubuntu SMP Fri Apr 8 09:10:54 UTC 2022
+Python: 3.8.10
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.2
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:071fca403815fcb45df20f1ce22816726c749983ffc019cee6a941d33a1a26dc
+size 230666

results.json

@@ -0,0 +1 @@
+{"mean_reward": 52.06303603704921, "std_reward": 98.78919476361308, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T20:30:47.846947"}