lunar lander v2 PPO 12x100K steps

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: 270.56 +/- 22.38
+      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
+
+...
+```

bguan_ppo_lunarlander_220924A.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52938962dbe6db3bfcc3888b19688097fdd7118d3dc9c2db16e32c3070cb6573
+size 147054

bguan_ppo_lunarlander_220924A/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.1a4

bguan_ppo_lunarlander_220924A/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 0x7f5c11875f80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5c1187c050>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5c1187c0e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5c1187c170>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5c1187c200>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5c1187c290>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5c1187c320>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5c1187c3b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5c1187c440>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5c1187c4d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5c1187c560>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5c118b9e40>"
+    },
+    "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": 114688,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1664059285147970939,
+    "learning_rate": 0.0005,
+    "tensorboard_log": "logs",
+    "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": -0.1468799999999999,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 364,
+    "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
+}

bguan_ppo_lunarlander_220924A/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80e09027c6eadb63c0ab12297fac0937bf67c2a922b3dab0d906d21613bbad6b
+size 87865

bguan_ppo_lunarlander_220924A/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94f94dfd034c6441a4c7abe2c19a06bd1a8c2657e060b8cb7771fb6de3222bf5
+size 43201

bguan_ppo_lunarlander_220924A/pytorch_variables.pth

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

bguan_ppo_lunarlander_220924A/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.14
+Stable-Baselines3: 1.6.1a4
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

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 0x7f5c11875f80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5c1187c050>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5c1187c0e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5c1187c170>", "_build": "<function ActorCriticPolicy._build at 0x7f5c1187c200>", "forward": "<function ActorCriticPolicy.forward at 0x7f5c1187c290>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5c1187c320>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5c1187c3b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5c1187c440>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5c1187c4d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5c1187c560>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5c118b9e40>"}, "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": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1664059285147970939, "learning_rate": 0.0005, "tensorboard_log": "logs", "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": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 364, "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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.14", "Stable-Baselines3": "1.6.1a4", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 270.5601603582439, "std_reward": 22.37697072075421, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-24T22:49:55.888206"}