first version
Browse files- LunarLander-V2-ppo.zip +3 -0
- LunarLander-V2-ppo/_stable_baselines3_version +1 -0
- LunarLander-V2-ppo/data +94 -0
- LunarLander-V2-ppo/policy.optimizer.pth +3 -0
- LunarLander-V2-ppo/policy.pth +3 -0
- LunarLander-V2-ppo/pytorch_variables.pth +3 -0
- LunarLander-V2-ppo/system_info.txt +7 -0
- README.md +36 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
LunarLander-V2-ppo.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:4ebaf8b13bf765f1ffd57190eca75ccde81cb82b6e7df52c46f8d887bf2c27ac
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size 147141
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LunarLander-V2-ppo/_stable_baselines3_version
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1.6.0
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LunarLander-V2-ppo/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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"__module__": "stable_baselines3.common.policies",
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"__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 ",
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f5ab61bd7a0>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5ab61bd830>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5ab61bd8c0>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5ab61bd950>",
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"_build": "<function ActorCriticPolicy._build at 0x7f5ab61bd9e0>",
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"forward": "<function ActorCriticPolicy.forward at 0x7f5ab61bda70>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5ab61bdb00>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7f5ab61bdb90>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5ab61bdc20>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5ab61bdcb0>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5ab61bdd40>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc_data object at 0x7f5ab618a810>"
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},
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"verbose": 1,
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"policy_kwargs": {},
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"dtype": "float32",
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"_shape": [
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"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
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"high": "[inf inf inf inf inf inf inf inf]",
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"bounded_below": "[False False False False False False False False]",
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"bounded_above": "[False False False False False False False False]",
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"_np_random": null
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},
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"n_envs": 16,
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"num_timesteps": 524288,
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"_total_timesteps": 500000,
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"n_steps": 2048,
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"vf_coef": 0.5,
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":type:": "<class 'function'>",
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|
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},
|
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|
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"normalize_advantage": true,
|
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+
"target_kl": null
|
94 |
+
}
|
LunarLander-V2-ppo/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:cfee67f2e4321749a8ac69ec935a21c6dae7d298ccc1391cae5d2a5e5f00a6d5
|
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+
size 87865
|
LunarLander-V2-ppo/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:65451e4b0f10235620cc4e6d3a2d73e2f0ade4b7daf7fe1544c8ae9a97c722c9
|
3 |
+
size 43201
|
LunarLander-V2-ppo/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
LunarLander-V2-ppo/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
2 |
+
Python: 3.7.13
|
3 |
+
Stable-Baselines3: 1.6.0
|
4 |
+
PyTorch: 1.12.0+cu113
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
README.md
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
|
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|
|
|
|
|
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 |
+
- metrics:
|
12 |
+
- type: mean_reward
|
13 |
+
value: 239.43 +/- 17.03
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: LunarLander-v2
|
20 |
+
type: LunarLander-v2
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **LunarLander-v2**
|
24 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
25 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
26 |
+
|
27 |
+
## Usage (with Stable-baselines3)
|
28 |
+
TODO: Add your code
|
29 |
+
|
30 |
+
|
31 |
+
```python
|
32 |
+
from stable_baselines3 import ...
|
33 |
+
from huggingface_sb3 import load_from_hub
|
34 |
+
|
35 |
+
...
|
36 |
+
```
|
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 0x7f5ab61bd7a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5ab61bd830>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5ab61bd8c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5ab61bd950>", "_build": "<function 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{"mean_reward": 239.42538904910035, "std_reward": 17.031289168477898, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-15T17:35:35.735720"}
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