first tryout

.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: 228.88 +/- 19.90
+      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 0x7f74c03b30e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f74c03b3170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74c03b3200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f74c03b3290>", "_build": "<function ActorCriticPolicy._build at 0x7f74c03b3320>", "forward": "<function ActorCriticPolicy.forward at 0x7f74c03b33b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f74c03b3440>", "_predict": "<function ActorCriticPolicy._predict at 0x7f74c03b34d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f74c03b3560>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f74c03b35f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f74c03b3680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f74c03ff690>"}, "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": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652527660.0600474, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAA0yIT4F37W7C4X/M3bamrIv1w29xcAStAAAgD8AAIA/zd1NPo4QCT+CDiq+M/u3vr8A6rqIvdG8AAAAAAAAAACaLP889gB7uv0D0jqTbrs1+owJu5ib9bkAAIA/AACAP2j28r7rMwW+//4/vZycObzSocg+PeP0OwAAgD8AAIA/IB9mPvBukz70NA08QcZgvjZHFj6wFai9AAAAAAAAAADzccW99oQeutKFMTonucQ19+9aO7MST7kAAIA/AACAPw132D2Yv74/vtMdPxTapjw7Vm89lfpvPgAAAAAAAAAAvTFxvqzZ1Tyq/cu6ZCeFOfC5ar5I9Aw6AACAPwAAgD/av4q9j+ZfuhWieDnEXF40OCpOOU0ZkrgAAIA/AACAPxqntj0paHu6xgrFOSYrEDZIdai6vczmuAAAgD8AAIA//elvvhTC27zIija7IayTuUdjQj5gbWM6AACAPwAAgD/G91Q+70TaPmJHFb4kA3q+OiSePOU0/rwAAAAAAAAAAHPcsD326AW6FY9lOa+7hzR4OA87UnSDuAAAgD8AAAAATeKzPXvKirpdrs07pBUMtiJuJrpylwC1AACAPwAAAADtRws+cX1ru7OLkbt69eU4YMinvDOasToAAIA/AACAP03ZBz1Ik426Hhg5OtryojQ1tes529ZWuQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 160, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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"}}

ll2-ppo-default.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10a6fee7bdd56105f62bac476c58ca2a5618ac5f5f5169ae30765cfbf900bf5e
+size 144094

ll2-ppo-default/_stable_baselines3_version

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

ll2-ppo-default/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 0x7f74c03b30e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f74c03b3170>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74c03b3200>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f74c03b3290>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f74c03b3320>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f74c03b33b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f74c03b3440>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f74c03b34d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f74c03b3560>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f74c03b35f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f74c03b3680>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f74c03ff690>"
+    },
+    "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": 524288,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652527660.0600474,
+    "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": -0.04857599999999995,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 160,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ll2-ppo-default/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:afe49283d6f29c626e8ddb334125232121bce386bb3037d3b9d04574a27d43da
+size 84893

ll2-ppo-default/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:098a905c97476c69b07c5fc55cc62d4331530b1b8190a9625d0b4c2d7e97fe18
+size 43201

ll2-ppo-default/pytorch_variables.pth

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

ll2-ppo-default/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:f0711aff5be31276001920ee623bc2f48e20861697ec3a31a5034f8ffe22d344
+size 225287

results.json

@@ -0,0 +1 @@
+{"mean_reward": 228.8789187053382, "std_reward": 19.899671023013266, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-14T11:53:29.576155"}