First trained model upload

.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_v1
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 226.29 +/- 14.66
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **PPO_v1** Agent playing **LunarLander-v2**
+  This is a trained model of a **PPO_v1** 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 0x7f5f04092440>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5f040924d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5f04092560>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5f040925f0>", "_build": "<function ActorCriticPolicy._build at 0x7f5f04092680>", "forward": "<function ActorCriticPolicy.forward at 0x7f5f04092710>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5f040927a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5f04092830>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5f040928c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5f04092950>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5f040929e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5f04069060>"}, "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": 1651691197.202681, "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:": "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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:e4ae3ccb9d75e6dbb195ebae3a642f1644aa2b378c67bf01d4e48f57ad7c12a1
+size 144023

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 0x7f5f04092440>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5f040924d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5f04092560>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5f040925f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5f04092680>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5f04092710>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5f040927a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5f04092830>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5f040928c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5f04092950>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5f040929e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5f04069060>"
+    },
+    "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": 1651691197.202681,
+    "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:": "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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:ea912a5ca789df8b9e48cd23b7e374260b2bdf010ba37a8ff6237d542c1ecab8
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f16bf36da56321900cbd5b964b87c5c192752705e8fc8e9d57f273bb0467637c
+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:b9f71c167ef48ba53c3e97a91271b132082c0f873321314c3e925c5c78db9f66
+size 257841

results.json

@@ -0,0 +1 @@
+{"mean_reward": 226.29358949917682, "std_reward": 14.662190930779046, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-04T19:26:23.479007"}