PPO for Lunar Lander Environment

.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 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 242.35 +/- 25.06
+      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
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f7e1ba34170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7e1ba34200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7e1ba34290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7e1ba34320>", "_build": "<function ActorCriticPolicy._build at 0x7f7e1ba343b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f7e1ba34440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7e1ba344d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7e1ba34560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7e1ba345f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7e1ba34680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7e1ba34710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7e1ba7f8d0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gASVwwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBVudW1weS5jb3JlLm11bHRpYXJyYXmUjAxfcmVjb25zdHJ1Y3SUk5RoBowHbmRhcnJheZSTlEsAhZRDAWKUh5RSlChLAUsIhZRoColDIAAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lHSUYowEaGlnaJRoEmgUSwCFlGgWh5RSlChLAUsIhZRoColDIAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/lHSUYowNYm91bmRlZF9iZWxvd5RoEmgUSwCFlGgWh5RSlChLAUsIhZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDCAAAAAAAAAAAlHSUYowNYm91bmRlZF9hYm92ZZRoEmgUSwCFlGgWh5RSlChLAUsIhZRoKolDCAAAAAAAAAAAlHSUYowKX25wX3JhbmRvbZROdWIu", "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1653831591.6515734, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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"}}

lunar_lander_ppo_1_million.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26e7f7884d73eadd820b858e3426dce56c61a1a39f40aae4c80a305154957de8
+size 144154

lunar_lander_ppo_1_million/_stable_baselines3_version

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

lunar_lander_ppo_1_million/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7f7e1ba34170>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7e1ba34200>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7e1ba34290>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7e1ba34320>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7e1ba343b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7e1ba34440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7e1ba344d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7e1ba34560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7e1ba345f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7e1ba34680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7e1ba34710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f7e1ba7f8d0>"
+    },
+    "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1653831591.6515734,
+    "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
+    },
+    "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_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
+}

lunar_lander_ppo_1_million/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5583252ca0dbe93359eb32f2a541a803ecd3044a02e7ebe773496f35254405d0
+size 84829

lunar_lander_ppo_1_million/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1abc45421c68cc46cc3f02960220526551033b994bc95caaa449507be729ce01
+size 43201

lunar_lander_ppo_1_million/pytorch_variables.pth

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

lunar_lander_ppo_1_million/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:a5b77cb3e2cbf131bca2557f93dba1e9b9ccb875cafa085fce6cf4afec721644
+size 213147

results.json

@@ -0,0 +1 @@
+{"mean_reward": 242.3463143560924, "std_reward": 25.05683939901166, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-29T14:01:26.732177"}