Class1 LunarLander-v2 excercise1

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: 268.13 +/- 18.35
+      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 0x7f07bb26ec10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f07bb26eca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f07bb26ed30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f07bb26edc0>", "_build": "<function ActorCriticPolicy._build at 0x7f07bb26ee50>", "forward": "<function ActorCriticPolicy.forward at 0x7f07bb26eee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f07bb26ef70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f07bb272040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f07bb2720d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f07bb272160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f07bb2721f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f07bb272280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f07bb26ac30>"}, "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": 1673705130463530026, "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-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "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 sha256:743f4293335fbcc0bf66d617a30dd379bb05d40f0fd595781e21add7d7d3557c
+size 147408

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7f07bb26ec10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f07bb26eca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f07bb26ed30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f07bb26edc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f07bb26ee50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f07bb26eee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f07bb26ef70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f07bb272040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f07bb2720d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f07bb272160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f07bb2721f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f07bb272280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f07bb26ac30>"
+    },
+    "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": 1673705130463530026,
+    "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-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de4f3a869175fb2bd3c1a3a64da71187d621f58db1ec178502b7cf8254af692b
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea190d6de33fec37233d8e2bc2dd089570e1a2ae82a50fea493b7d7b1854ad0a
+size 43393

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-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 268.12830573886487, "std_reward": 18.353501216813097, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-14T14:42:04.421011"}