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: 248.95 +/- 22.94
+      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 0x7de868b8c1f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7de868b8c280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7de868b8c310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7de868b8c3a0>", "_build": "<function ActorCriticPolicy._build at 0x7de868b8c430>", "forward": "<function ActorCriticPolicy.forward at 0x7de868b8c4c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7de868b8c550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7de868b8c5e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7de868b8c670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7de868b8c700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7de868b8c790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7de868b8c820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7de868b23300>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699603828011483210, "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "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 sha256:150c95835b8d4411f845fefcc720fe5e1cd16012d211aeaf806a616be857dddc
+size 148050

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 0x7de868b8c1f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7de868b8c280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7de868b8c310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7de868b8c3a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7de868b8c430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7de868b8c4c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7de868b8c550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7de868b8c5e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7de868b8c670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7de868b8c700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7de868b8c790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7de868b8c820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7de868b23300>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1699603828011483210,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAADOFobwprH+6BvbRuykw4zeAHwY7w3IXtwAAgD8AAIA/s4jEvXuaq7rkQ5Q77xNANfN2jrq++Km6AACAPwAAAADaSLi9XJNLukaJWrk3o0O0nk0curgNgTgAAIA/AAAAAA2A8r14FLw9UXYEPtziM76DlkU9/hmIvAAAAAAAAAAAGqVrPRQghLraXsg5Wlc6NjooNTpqOOq4AACAPwAAgD8aRVC97Bn9uel4nrotoqu2YtmdOWdetjkAAIA/AACAPwArMD2uSYu6IAYsuulcEbWHKLq6gLhHOQAAgD8AAIA/TZp9vXtOiLoeorY4ZnmyMzI2GboKetS3AACAPwAAgD8zNrM8SBuluv3W/jePc/EyPEydOPBnErcAAIA/AACAP0ArvL2P1nu620vcuomItLONRbi6m479OQAAgD8AAIA/pi+zvVwvc7phnEu6bQyCt4H5Krs6b0g5AACAPwAAgD8mdpw9ixXLPiqzML5G9Yu+7x2gvbtmdL0AAAAAAAAAADOT4zx75pW6+lkTN8H+EDLzUIy6xg0qtgAAgD8AAIA/M0vEu+Ewjbpn6J86kZKXNd2AtDk8xrm5AACAPwAAgD9zvJc9rkGBuiOQvjqP5qU1++B1ujN53rkAAIA/AACAPzPP7zyP/gA/FFAnvhccg77RnKO9Im6mPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95fa90df563197e01a513ace6f8ee1810a8f12fcedb3f63207d63215b7cf967f
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4bfd9717606e6c7afad315cd114648aba0aadb24636060dba2f6cb812c2edf7b
+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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 248.9514016558538, "std_reward": 22.94295141364874, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-10T08:35:58.448580"}