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: 121.87 +/- 85.46
+      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 0x7f0cea07c170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0cea07c200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0cea07c290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0cea07c320>", "_build": "<function ActorCriticPolicy._build at 0x7f0cea07c3b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f0cea07c440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0cea07c4d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0cea07c560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0cea07c5f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0cea07c680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0cea07c710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0cea138f00>"}, "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": 1652738977.1583235, "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:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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 sha256:83795e911c57a800d77152b53c1687acfee5b5ac7dd2c5c2cd53cb5de6898f94
+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 0x7f0cea07c170>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0cea07c200>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0cea07c290>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0cea07c320>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f0cea07c3b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f0cea07c440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0cea07c4d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f0cea07c560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0cea07c5f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0cea07c680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0cea07c710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f0cea138f00>"
+    },
+    "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": 1652738977.1583235,
+    "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:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "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 sha256:5cfe61a40ec653e8ca7a7945eb09d623da5e72d41dc0ca1115e2f5ed0acb0c04
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29a6d35ba5be6bf7c919173cea41440058aa0c06e49ac0415f98e366830d33e7
+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 sha256:202ba1144871c12ad08a6a1f26d4803a9332024ca6912c1d4fb359f826393f2d
+size 239293

results.json

@@ -0,0 +1 @@
+{"mean_reward": 121.87099026222938, "std_reward": 85.4614741513527, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-16T22:23:21.188200"}