Unit 1: PPO LunarLander agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 247.71 +/- 21.09
+      name: mean_reward
+      verified: false
+---
+
+# **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:": "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f397d84a940>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f397d84a9d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f397d84aa60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f397d84aaf0>", "_build": "<function ActorCriticPolicy._build at 0x7f397d84ab80>", "forward": "<function ActorCriticPolicy.forward at 0x7f397d84ac10>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f397d84aca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f397d84ad30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f397d84adc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f397d84ae50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f397d84aee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f397d84af70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f397d8456f0>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677736213056055746, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf10ec9bcf98b073af8349074fc14bbd64e316ae753afd6ed1b6cb0ea10ee110
+size 147416

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f397d84a940>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f397d84a9d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f397d84aa60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f397d84aaf0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f397d84ab80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f397d84ac10>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f397d84aca0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f397d84ad30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f397d84adc0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f397d84ae50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f397d84aee0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f397d84af70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f397d8456f0>"
+    },
+    "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": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677736213056055746,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa6086fc2507338f12404df52374ef3b64c6849c6537e51e41024ff308dfb66c
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ceed8d89205eb02cfe3268ea9bb7686680ada225add242aa6f0490154998d238
+size 43393

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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 247.70989224844016, "std_reward": 21.094357907205083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-02T06:16:48.714768"}