test

.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

Mlp.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57b145344a876a0923bc7dd590ca9b5d116eb366f79dda97ed810d3322861b61
+size 144098

Mlp/_stable_baselines3_version

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

Mlp/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 0x7fcc02e99e60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcc02e99ef0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcc02e99f80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcc02ea0050>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcc02ea00e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcc02ea0170>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc02ea0200>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcc02ea0290>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcc02ea0320>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc02ea03b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc02ea0440>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fcc02e73300>"
+    },
+    "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": 1651742672.9336817,
+    "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
+}

Mlp/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:403c174eed7a3d17aa3daa5bbe05a4726866aff3ff95556c16698259fa581175
+size 84893

Mlp/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a0d15ca1ad4e84b9abc07956e2302be14f71eb955adb326cb536687aaec2b92
+size 43201

Mlp/pytorch_variables.pth

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

Mlp/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: 189.94 +/- 100.30
+      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 0x7fcc02e99e60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcc02e99ef0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcc02e99f80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcc02ea0050>", "_build": "<function ActorCriticPolicy._build at 0x7fcc02ea00e0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcc02ea0170>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc02ea0200>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcc02ea0290>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcc02ea0320>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc02ea03b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc02ea0440>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcc02e73300>"}, "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": 1651742672.9336817, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAALNGwD09Chw4lyVMua3KVLS+8Wq7ZlZwOAAAgD8AAIA/mqyRPuEi3jvCuf68GukfPWbQmz00zwq+AACAPwAAgD9GdXQ+D4NYvEOJc7iWx1g20mW1vewojzcAAIA/AACAP5qrMz4FWaw8/hiruptKIbmbcjw+COb4OQAAgD8AAIA/g7VuvlKshjp9q+a2cgNMM8obJbziWAs2AACAPwAAgD9mZnK9H0XHuaTRRbkaanG0m8NsO82FZTgAAIA/AACAP7C7Vb7ri30/JUdmvk+Jp77NdPu9Msp4PQAAAAAAAAAAhus4PrC0ID/to5o9RTvCvhfkKj1NoA88AAAAAAAAAAAgYiK+bNXWuw3Pz7qA9te4QQ42PevXqjkAAIA/AACAP62oEL64hom5AW6qOXETp7UbIqu7QovCuAAAgD8AAIA/gKaxPa4n5riMmLu3oe48NPsR7jglONk2AACAPwAAgD9IBrO+X2iMPHbZ87uzN9q58OUCvY0cZDsAAIA/AACAP4DIfD6iC2k/jaWCPt99CL9mc/09DkEnvQAAAAAAAAAACvmAPtwFZbwe8d46BxnUuKuSyr2dJwW6AACAPwAAgD8Gul4+ivYIPDtHhbrvGkq4CRmcPfSNmTkAAIA/AACAPw7rj77QEJM+ducGPvsNEb7aBYe800VgPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "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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 sha256:3514146962929576186b026758786850484170bddb2407e36e67691752937abc
+size 206220

results.json

@@ -0,0 +1 @@
+{"mean_reward": 189.93981295376892, "std_reward": 100.30202370656865, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T09:55:38.435149"}