unit 1 exercise

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: 249.29 +/- 41.33
+      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 0x7fea688a9ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fea688a9d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fea688a9dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fea688a9e50>", "_build": "<function ActorCriticPolicy._build at 0x7fea688a9ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fea688a9f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fea688ac040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fea688ac0d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fea688ac160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fea688ac1f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fea688ac280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fea688ac310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fea688aac40>"}, "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": 1679032912561274731, "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

my_model_LunarLander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d67cb8b59382dacd60ef5c3d0b50eb268c3bb3f53443aff7277f5e51716b787
+size 147413

my_model_LunarLander/_stable_baselines3_version

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

my_model_LunarLander/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 0x7fea688a9ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fea688a9d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fea688a9dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fea688a9e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fea688a9ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fea688a9f70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fea688ac040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fea688ac0d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fea688ac160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fea688ac1f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fea688ac280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fea688ac310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fea688aac40>"
+    },
+    "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": 1679032912561274731,
+    "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
+}

my_model_LunarLander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89ef923cffdc7826c792359cd1c89c4e107f6b48280a0be98c5c7422e5679a7d
+size 87929

my_model_LunarLander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9593c83621e28a635411c67de8e56fb9def68f670fe7765a7f05168bd9a35d1b
+size 43393

my_model_LunarLander/pytorch_variables.pth

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

my_model_LunarLander/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 249.28540173040977, "std_reward": 41.3338316419639, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-17T06:27:56.129505"}