Reinforcement learning class 1

.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: 225.45 +/- 14.86
+      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 0x7fba96bc9320>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fba96bc93b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fba96bc9440>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fba96bc94d0>", "_build": "<function ActorCriticPolicy._build at 0x7fba96bc9560>", "forward": "<function ActorCriticPolicy.forward at 0x7fba96bc95f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fba96bc9680>", "_predict": "<function ActorCriticPolicy._predict at 0x7fba96bc9710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fba96bc97a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fba96bc9830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fba96bc98c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fba96c09d50>"}, "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": 1652481849.2438436, "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, "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 sha256:2eeebe5ad6697b2146d093a5aa7c5e3480934dfe7cbdf9fe3d5197a698f318dc
+size 144110

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 0x7fba96bc9320>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fba96bc93b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fba96bc9440>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fba96bc94d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fba96bc9560>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fba96bc95f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fba96bc9680>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fba96bc9710>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fba96bc97a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fba96bc9830>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fba96bc98c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fba96c09d50>"
+    },
+    "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": 1652481849.2438436,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d55051c1d1c7f1449f3c7b89f4b9c9597f4fba4c57019579be465d0cb4f96fca
+size 84893

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55d07fefe6056296fdedc8959e0342b9a6f2501d6093f546fe5609f911065d62
+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 sha256:71be47f767a94d20e7d4d43c2e4aad3e11aed3cbeb7481a9df876c40345a44ee
+size 254394

results.json

@@ -0,0 +1 @@
+{"mean_reward": 225.4527629114216, "std_reward": 14.855871410997137, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-13T22:59:13.433556"}