just finished this part 1 of the RL course, EZ Clap 228 mean reward, very ez indeed

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: 136.06 +/- 125.48
+      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 0x7f3df8ee6ef0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3df8ee6f80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3df8eed050>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3df8eed0e0>", "_build": "<function ActorCriticPolicy._build at 0x7f3df8eed170>", "forward": "<function ActorCriticPolicy.forward at 0x7f3df8eed200>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3df8eed290>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3df8eed320>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3df8eed3b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3df8eed440>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3df8eed4d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f3df8f405d0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1669252367928976710, "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": 124, "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-LunearLander-v2-aspectcisco.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c057397bf13ffb16b5b9ef0c931ac14b334fbb42b23699a70d896684d77f1ea
+size 147138

ppo-LunearLander-v2-aspectcisco/_stable_baselines3_version

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

ppo-LunearLander-v2-aspectcisco/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 0x7f3df8ee6ef0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3df8ee6f80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3df8eed050>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3df8eed0e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3df8eed170>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3df8eed200>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3df8eed290>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3df8eed320>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3df8eed3b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3df8eed440>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3df8eed4d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f3df8f405d0>"
+    },
+    "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1669252367928976710,
+    "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": 124,
+    "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-LunearLander-v2-aspectcisco/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2eb621c5f7a0a363643598473c23b4e1e5182f9db43ada0af964f13403a37303
+size 87865

ppo-LunearLander-v2-aspectcisco/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:226a6f877f4a0530ebec4b0d5123c107e81a90cd7535a7c094803823ebaa92ae
+size 43201

ppo-LunearLander-v2-aspectcisco/pytorch_variables.pth

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

ppo-LunearLander-v2-aspectcisco/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": 136.0605593344986, "std_reward": 125.4769497634111, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-11-24T01:27:44.942195"}