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: -120.71 +/- 17.76
+      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 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 0x7f04b9beed40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f04b9beedd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f04b9beee60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f04b9beeef0>", "_build": "<function ActorCriticPolicy._build at 0x7f04b9beef80>", "forward": "<function ActorCriticPolicy.forward at 0x7f04b9bf6050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f04b9bf60e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f04b9bf6170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f04b9bf6200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f04b9bf6290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f04b9bf6320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f04b9c3e9c0>"}, "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": 16384, "_total_timesteps": 5000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1667999283184901117, "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": -2.2768, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 4, "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": "3.7.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5b9453e4f5c8a37c5d2e1410a9164d3be9dded7cb5dfbe24e3d858c17b0332d
+size 147003

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2/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 0x7f04b9beed40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f04b9beedd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f04b9beee60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f04b9beeef0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f04b9beef80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f04b9bf6050>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f04b9bf60e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f04b9bf6170>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f04b9bf6200>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f04b9bf6290>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f04b9bf6320>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f04b9c3e9c0>"
+    },
+    "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": 16384,
+    "_total_timesteps": 5000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1667999283184901117,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "_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": -2.2768,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 4,
+    "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 sha256:5cbd7c22cc7847108c8c0055cbb1c9149eee635f7b4cab1b6e5dd27568ca9017
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3da54671cb81c4d1c0c5de99df79caaf251b77d0730d0d593e9a7fdad40ddbc3
+size 43201

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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": -120.71336694940692, "std_reward": 17.7550889211137, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-11-09T13:09:31.847162"}