Baseline 1M epochs

.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

LunarLander-v2-PPO.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d336a70bf648ae524d98932383c321e9b6aded1e40127e7e397bfdb8fd216ae
+size 144029

LunarLander-v2-PPO/_stable_baselines3_version

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

LunarLander-v2-PPO/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 0x7f394c0eda70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f394c0edb00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f394c0edb90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f394c0edc20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f394c0edcb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f394c0edd40>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f394c0eddd0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f394c0ede60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f394c0edef0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f394c0edf80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f394c0f2050>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f394c142570>"
+    },
+    "verbose": true,
+    "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": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652546156.3737378,
+    "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": 248,
+    "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
+}

LunarLander-v2-PPO/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55b740941f0404c2d30177d190e4b63af18f8a70eb460e017ed37da40f201b0d
+size 84829

LunarLander-v2-PPO/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3533cd94662f9011787667c580acb9bcea7e533b73cdd2d2e91fe34ca279c710
+size 43201

LunarLander-v2-PPO/pytorch_variables.pth

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

LunarLander-v2-PPO/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

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: 263.46 +/- 22.89
+      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 0x7f394c0eda70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f394c0edb00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f394c0edb90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f394c0edc20>", "_build": "<function ActorCriticPolicy._build at 0x7f394c0edcb0>", "forward": "<function ActorCriticPolicy.forward at 0x7f394c0edd40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f394c0eddd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f394c0ede60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f394c0edef0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f394c0edf80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f394c0f2050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f394c142570>"}, "verbose": true, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652546156.3737378, "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": 248, "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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:407a68a4c25e8d376a23ffc7f7078d614f96d9982ef1ed12e619c7ba40543d18
+size 208446

results.json

@@ -0,0 +1 @@
+{"mean_reward": 263.46341418885197, "std_reward": 22.886157125894723, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-14T16:54:38.754838"}