upload mark2 version ppo LunarLander-v2 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: 275.95 +/- 13.49
+      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 0x7fca06dd5160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fca06dd51f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fca06dd5280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fca06dd5310>", "_build": "<function ActorCriticPolicy._build at 0x7fca06dd53a0>", "forward": "<function ActorCriticPolicy.forward at 0x7fca06dd5430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fca06dd54c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fca06dd5550>", "_predict": "<function ActorCriticPolicy._predict at 0x7fca06dd55e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fca06dd5670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fca06dd5700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fca06dd5790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fca06dbecf0>"}, "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": 1504000, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676615214481900303, "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": -0.002666666666666595, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 376, "n_steps": 1000, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2-Mark2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fda07d9822ed9d7e759427df1278b9015f60a3abb42a0e42bf59840574af5b1
+size 147400

ppo-LunarLander-v2-Mark2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2-Mark2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7fca06dd5160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fca06dd51f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fca06dd5280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fca06dd5310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fca06dd53a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fca06dd5430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fca06dd54c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fca06dd5550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fca06dd55e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fca06dd5670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fca06dd5700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fca06dd5790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fca06dbecf0>"
+    },
+    "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": 1504000,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676615214481900303,
+    "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": -0.002666666666666595,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 376,
+    "n_steps": 1000,
+    "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-Mark2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a59fe4da0b628d79484a5918ec00e924701a7e138452e25fcca6a26f2020c78
+size 87929

ppo-LunarLander-v2-Mark2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b21674a57f6d43641ce4739be72baaa31fbb386acd6a646e30896aee8357fa1d
+size 43393

ppo-LunarLander-v2-Mark2/pytorch_variables.pth

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

ppo-LunarLander-v2-Mark2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 275.9498280186539, "std_reward": 13.492901219031662, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-17T07:05:22.690975"}