Upload PPO LunarLander-v2 trained 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: 247.63 +/- 19.38
+      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 0x7f7694ccaee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7694ccaf70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7694cce040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7694cce0d0>", "_build": "<function ActorCriticPolicy._build at 0x7f7694cce160>", "forward": "<function ActorCriticPolicy.forward at 0x7f7694cce1f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7694cce280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7694cce310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7694cce3a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7694cce430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7694cce4c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7694cce550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7694cc8b10>"}, "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": 1676390241377114894, "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": 252, "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:": "gAWVhAIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMUi9yb290L3JsLWNvdXJzZS92ZW52L2xpYi9weXRob24zLjgvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UaAx1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgefZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.0-137-generic-x86_64-with-glibc2.29 # 154-Ubuntu SMP Thu Jan 5 17:03:22 UTC 2023", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbd030e1d5eed978cee973f253589781b5ce8f69d2e686e4d1ac27a5fbe54b2b
+size 147264

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 0x7f7694ccaee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7694ccaf70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7694cce040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7694cce0d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7694cce160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7694cce1f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7694cce280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7694cce310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7694cce3a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7694cce430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7694cce4c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7694cce550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f7694cc8b10>"
+    },
+    "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": 1676390241377114894,
+    "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": 252,
+    "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:b03d1f6469a31cea7f53c414cb8c6e1d5e54be0344e37ba80d244545afa1532b
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c4a1677755166ef3e1ac0b5bb91b7a330c06624ee89729aec58751a852a1753
+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-5.4.0-137-generic-x86_64-with-glibc2.29 # 154-Ubuntu SMP Thu Jan 5 17:03:22 UTC 2023
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.2
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 247.62615405728707, "std_reward": 19.378165369206812, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-14T17:08:17.916152"}