File size: 3,304 Bytes
fc20291 2c219f7 fc20291 2c219f7 fc20291 2c219f7 fc20291 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
library_name: stable-baselines3
tags:
- donkey-mountain-track-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- metrics:
- type: mean_reward
value: 363.88 +/- 0.94
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: donkey-mountain-track-v0
type: donkey-mountain-track-v0
---
# **TQC** Agent playing **donkey-mountain-track-v0**
This is a trained model of a **TQC** agent playing **donkey-mountain-track-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo tqc --env donkey-mountain-track-v0 -orga araffin -f logs/
python enjoy.py --algo tqc --env donkey-mountain-track-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo tqc --env donkey-mountain-track-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo tqc --env donkey-mountain-track-v0 -f logs/ -orga araffin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 200000),
('callback',
[{'utils.callbacks.ParallelTrainCallback': {'gradient_steps': 200}},
'utils.callbacks.LapTimeCallback']),
('ent_coef', 'auto'),
('env_wrapper',
[{'gym.wrappers.time_limit.TimeLimit': {'max_episode_steps': 10000}},
'ae.wrapper.AutoencoderWrapper',
{'utils.wrappers.HistoryWrapper': {'horizon': 2}}]),
('gamma', 0.99),
('gradient_steps', 256),
('learning_rate', 0.00073),
('learning_starts', 500),
('n_timesteps', 2000000.0),
('normalize', "{'norm_obs': True, 'norm_reward': False}"),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(log_std_init=-3, net_arch=[256, 256], n_critics=2, '
'use_expln=True)'),
('sde_sample_freq', 16),
('tau', 0.02),
('train_freq', 200),
('use_sde', True),
('use_sde_at_warmup', True),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
# Environment Arguments
```python
{'conf': {'cam_resolution': (120, 160, 3),
'car_config': {'body_rgb': (226, 112, 18),
'body_style': 'donkey',
'car_name': 'Toni',
'font_size': 40},
'frame_skip': 1,
'host': 'localhost',
'level': 'mountain_track',
'log_level': 20,
'max_cte': 16,
'port': 9091,
'start_delay': 5.0},
'min_throttle': -0.2,
'steer': 0.3}
```
|