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: 254.54 +/- 14.04
+      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 0x79e66eb08550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79e66eb085e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79e66eb08670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79e66eb08700>", "_build": "<function ActorCriticPolicy._build at 0x79e66eb08790>", "forward": "<function ActorCriticPolicy.forward at 0x79e66eb08820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79e66eb088b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79e66eb08940>", "_predict": "<function ActorCriticPolicy._predict at 0x79e66eb089d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79e66eb08a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79e66eb08af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79e66eb08b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79e66ec65400>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1729818462014468271, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a850153f8fcc82c6a9195facf25734c57141e63a734e801f9f6a3cfec68b0afe
+size 148016

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 0x79e66eb08550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79e66eb085e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79e66eb08670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79e66eb08700>",
+        "_build": "<function ActorCriticPolicy._build at 0x79e66eb08790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x79e66eb08820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x79e66eb088b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79e66eb08940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x79e66eb089d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79e66eb08a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79e66eb08af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x79e66eb08b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x79e66ec65400>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1729818462014468271,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVrQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUaACMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFowEZnVuY5SMDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:102b66bbcf360117560ae1a3429094c6bac2bc1f14696da0910753a3615eea25
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18e925dc569f42a9b82c39fb177d2c5355ea72a5d53bf6869b5c9780fb787ea6
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.5.0+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.0
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 254.5367267972374, "std_reward": 14.04311007471858, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-10-25T01:31:49.032564"}