first_commit
Browse files- README.md +37 -0
- config.json +1 -0
- ppo_LunarLander-v2.zip +3 -0
- ppo_LunarLander-v2/_stable_baselines3_version +1 -0
- ppo_LunarLander-v2/data +94 -0
- ppo_LunarLander-v2/policy.optimizer.pth +3 -0
- ppo_LunarLander-v2/policy.pth +3 -0
- ppo_LunarLander-v2/pytorch_variables.pth +3 -0
- ppo_LunarLander-v2/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: stable-baselines3
|
| 3 |
+
tags:
|
| 4 |
+
- LunarLander-v2
|
| 5 |
+
- deep-reinforcement-learning
|
| 6 |
+
- reinforcement-learning
|
| 7 |
+
- stable-baselines3
|
| 8 |
+
model-index:
|
| 9 |
+
- name: PPO
|
| 10 |
+
results:
|
| 11 |
+
- task:
|
| 12 |
+
type: reinforcement-learning
|
| 13 |
+
name: reinforcement-learning
|
| 14 |
+
dataset:
|
| 15 |
+
name: LunarLander-v2
|
| 16 |
+
type: LunarLander-v2
|
| 17 |
+
metrics:
|
| 18 |
+
- type: mean_reward
|
| 19 |
+
value: -1339.88 +/- 1647.82
|
| 20 |
+
name: mean_reward
|
| 21 |
+
verified: false
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
| 25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
| 26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
| 27 |
+
|
| 28 |
+
## Usage (with Stable-baselines3)
|
| 29 |
+
TODO: Add your code
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
```python
|
| 33 |
+
from stable_baselines3 import ...
|
| 34 |
+
from huggingface_sb3 import load_from_hub
|
| 35 |
+
|
| 36 |
+
...
|
| 37 |
+
```
|
config.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 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 0x7f19cf78e790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f19cf78e820>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f19cf78e8b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f19cf78e940>", "_build": "<function ActorCriticPolicy._build at 0x7f19cf78e9d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f19cf78ea60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f19cf78eaf0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f19cf78eb80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f19cf78ec10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f19cf78eca0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f19cf78ed30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f19cf790060>"}, "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": 1, "num_timesteps": 10240, "_total_timesteps": 10000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671181215024930306, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJrQbD7orJo/cKwWPxQlCL/Wp1A+UwrtPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.02400000000000002, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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"}}
|
ppo_LunarLander-v2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:84d51cedebb2010bace2ab0b6b5ce23a676915372b3f49293511c533fbf84ecf
|
| 3 |
+
size 146420
|
ppo_LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.6.2
|
ppo_LunarLander-v2/data
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"policy_class": {
|
| 3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
| 4 |
+
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
| 5 |
+
"__module__": "stable_baselines3.common.policies",
|
| 6 |
+
"__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 ",
|
| 7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7f19cf78e790>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f19cf78e820>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f19cf78e8b0>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f19cf78e940>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f19cf78e9d0>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f19cf78ea60>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f19cf78eaf0>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f19cf78eb80>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f19cf78ec10>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f19cf78eca0>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f19cf78ed30>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f19cf790060>"
|
| 20 |
+
},
|
| 21 |
+
"verbose": 1,
|
| 22 |
+
"policy_kwargs": {},
|
| 23 |
+
"observation_space": {
|
| 24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
| 25 |
+
":serialized:": "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",
|
| 26 |
+
"dtype": "float32",
|
| 27 |
+
"_shape": [
|
| 28 |
+
8
|
| 29 |
+
],
|
| 30 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
| 31 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
| 32 |
+
"bounded_below": "[False False False False False False False False]",
|
| 33 |
+
"bounded_above": "[False False False False False False False False]",
|
| 34 |
+
"_np_random": null
|
| 35 |
+
},
|
| 36 |
+
"action_space": {
|
| 37 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
| 38 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
| 39 |
+
"n": 4,
|
| 40 |
+
"_shape": [],
|
| 41 |
+
"dtype": "int64",
|
| 42 |
+
"_np_random": null
|
| 43 |
+
},
|
| 44 |
+
"n_envs": 1,
|
| 45 |
+
"num_timesteps": 10240,
|
| 46 |
+
"_total_timesteps": 10000.0,
|
| 47 |
+
"_num_timesteps_at_start": 0,
|
| 48 |
+
"seed": null,
|
| 49 |
+
"action_noise": null,
|
| 50 |
+
"start_time": 1671181215024930306,
|
| 51 |
+
"learning_rate": 0.0003,
|
| 52 |
+
"tensorboard_log": null,
|
| 53 |
+
"lr_schedule": {
|
| 54 |
+
":type:": "<class 'function'>",
|
| 55 |
+
":serialized:": "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"
|
| 56 |
+
},
|
| 57 |
+
"_last_obs": {
|
| 58 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 59 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJrQbD7orJo/cKwWPxQlCL/Wp1A+UwrtPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
|
| 60 |
+
},
|
| 61 |
+
"_last_episode_starts": {
|
| 62 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 63 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
| 64 |
+
},
|
| 65 |
+
"_last_original_obs": null,
|
| 66 |
+
"_episode_num": 0,
|
| 67 |
+
"use_sde": false,
|
| 68 |
+
"sde_sample_freq": -1,
|
| 69 |
+
"_current_progress_remaining": -0.02400000000000002,
|
| 70 |
+
"ep_info_buffer": {
|
| 71 |
+
":type:": "<class 'collections.deque'>",
|
| 72 |
+
":serialized:": "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"
|
| 73 |
+
},
|
| 74 |
+
"ep_success_buffer": {
|
| 75 |
+
":type:": "<class 'collections.deque'>",
|
| 76 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 77 |
+
},
|
| 78 |
+
"_n_updates": 50,
|
| 79 |
+
"n_steps": 2048,
|
| 80 |
+
"gamma": 0.99,
|
| 81 |
+
"gae_lambda": 0.95,
|
| 82 |
+
"ent_coef": 0.0,
|
| 83 |
+
"vf_coef": 0.5,
|
| 84 |
+
"max_grad_norm": 0.5,
|
| 85 |
+
"batch_size": 64,
|
| 86 |
+
"n_epochs": 10,
|
| 87 |
+
"clip_range": {
|
| 88 |
+
":type:": "<class 'function'>",
|
| 89 |
+
":serialized:": "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"
|
| 90 |
+
},
|
| 91 |
+
"clip_range_vf": null,
|
| 92 |
+
"normalize_advantage": true,
|
| 93 |
+
"target_kl": null
|
| 94 |
+
}
|
ppo_LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d97edfce5d114eef07a44bb6b17adc40f629970cd0ef4b91ca22fceb9b432c27
|
| 3 |
+
size 87929
|
ppo_LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fb9435381d51185325fbaea251035638733283ae25ca560517e66293fc2e9863
|
| 3 |
+
size 43201
|
ppo_LunarLander-v2/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
| 3 |
+
size 431
|
ppo_LunarLander-v2/system_info.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
| 2 |
+
Python: 3.8.16
|
| 3 |
+
Stable-Baselines3: 1.6.2
|
| 4 |
+
PyTorch: 1.13.0+cu116
|
| 5 |
+
GPU Enabled: True
|
| 6 |
+
Numpy: 1.21.6
|
| 7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
|
Binary file (127 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": -1339.8777635268402, "std_reward": 1647.8182759184251, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-16T09:01:58.162077"}
|