Trained RL model for LunarLander-v2

PPO_agent_lunar.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:515bbc3792a6b97157f2647a1c512fbcbc6c82f7749a57a483d40b1fa0e4a397
+size 147190

PPO_agent_lunar/_stable_baselines3_version

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

PPO_agent_lunar/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 0x7fadd809bca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fadd809bd30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fadd809bdc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fadd809be50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fadd809bee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fadd809bf70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fadd80a0040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fadd80a00d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fadd80a0160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fadd80a01f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fadd80a0280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fadd80984b0>"
+    },
+    "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": 1671463499194806917,
+    "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": 248,
+    "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_agent_lunar/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e94dc74185c247cbd1ea1067587ca6095191f4a618504c719b9971c074b04f7
+size 87929

PPO_agent_lunar/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65f90fbd2e0aece9039922f109555b40e15e00ec20a64d8239da6008b52f6c83
+size 43201

PPO_agent_lunar/pytorch_variables.pth

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

PPO_agent_lunar/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

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: 269.84 +/- 17.12
+      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 0x7fadd809bca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fadd809bd30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fadd809bdc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fadd809be50>", "_build": "<function ActorCriticPolicy._build at 0x7fadd809bee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fadd809bf70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fadd80a0040>", "_predict": "<function ActorCriticPolicy._predict at 0x7fadd80a00d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fadd80a0160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fadd80a01f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fadd80a0280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fadd80984b0>"}, "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": 1671463499194806917, "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": 248, "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-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 269.837684982255, "std_reward": 17.11568195066011, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-19T16:01:21.506167"}