updated README
This commit is contained in:
parent
edc26e42ed
commit
0a3c8beec0
24
README.md
24
README.md
@ -7,27 +7,25 @@ Get dependencies:
|
||||
```
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
Train the agent on Walker Walk in DMC Vision:
|
||||
Run training on DMC Vision:
|
||||
```
|
||||
python3 dreamer.py --configs dmc_vision --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk
|
||||
```
|
||||
Train the agent on Walker Walk in DMC Proprio:
|
||||
```
|
||||
python3 dreamer.py --configs dmc_proprio --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk
|
||||
```
|
||||
Train the agent on Alien in Atari 100K:
|
||||
```
|
||||
python3 dreamer.py --configs atari100k --task atari_alien --logdir ./logdir/atari_alien
|
||||
```
|
||||
Train the agent on Crafter:
|
||||
```
|
||||
python3 dreamer.py --configs crafter --logdir ./logdir/crafter
|
||||
```
|
||||
Monitor results:
|
||||
```
|
||||
tensorboard --logdir ./logdir
|
||||
```
|
||||
|
||||
## Benchmarks
|
||||
So far, this repository allows testing the following benchmarks.
|
||||
| Environment | Observation | Action | Description |
|
||||
|-------------------|---|---|-----------------------|
|
||||
| [DMC Proprio](https://github.com/deepmind/dm_control) | State | Continuous | This benchmark contains 18 continuous control tasks with low-dimensional inputs and a budget of 500K environment steps. |
|
||||
| [DMC Vision](https://github.com/deepmind/dm_control) | Image | Continuous | This benchmark consists of 20 continuous control tasks where the agent receives only high-dimensional images as inputs and a budget of 1M environment steps. |
|
||||
| [Atari 100k](https://github.com/openai/atari-py) | Image | Discrete | This benchmark includes 26 Atari games and a budget of only 400K environment steps, amounting to 100K steps after action repeat or 2 hours of real time. |
|
||||
| [Crafter](https://github.com/danijar/crafter) | Image | Discrete | This survival environment evaluates diverse agent abilities, including exploration, reasoning, credit assignment, and generalization.|
|
||||
| [Memory Maze](https://github.com/jurgisp/memory-maze) | Image | Discrete | Memory Maze is a 3D benchmark with randomized mazes to evaluate RL agents' long-term memory.|
|
||||
|
||||
## Results
|
||||
#### DMC Vision
|
||||

|
||||
|
Loading…
x
Reference in New Issue
Block a user