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@ -17,25 +17,22 @@ tensorboard --logdir ./logdir
``` ```
## Benchmarks ## Benchmarks
So far, this repository allows testing the following benchmarks. So far, the following benchmarks can be used for testing.
| Environment | Observation | Action | Description | | Environment | Observation | Action | Budget | 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 Proprio](https://github.com/deepmind/dm_control) | State | Continuous | 500K | DeepMind Control Suite with low-dimensional inputs. |
| [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. | | [DMC Vision](https://github.com/deepmind/dm_control) | Image | Continuous |1M| DeepMind Control Suite with high-dimensional images inputs. |
| [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. | | [Atari 100k](https://github.com/openai/atari-py) | Image | Discrete |400K| 26 Atari games. |
| [Crafter](https://github.com/danijar/crafter) | Image | Discrete | This survival environment evaluates diverse agent abilities, including exploration, reasoning, credit assignment, and generalization.| | [Crafter](https://github.com/danijar/crafter) | Image | Discrete |1M| Survival environment to evaluates diverse agent abilities.|
| [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.| | [Memory Maze](https://github.com/jurgisp/memory-maze) | Image |Discrete |100M| 3D mazes to evaluate RL agents' long-term memory.|
## Results ## Results
#### DMC Vision
![dmcvision](https://github.com/NM512/dreamerv3-torch/assets/70328564/b710d217-2428-4fa0-8471-55e15ec5aa43)
#### Atari 100k
![atari100k](https://github.com/NM512/dreamerv3-torch/assets/70328564/0da6d899-d91d-44b4-a8c4-d5b37413aa11)
#### DMC Proprio #### DMC Proprio
![dmcproprio](https://github.com/NM512/dreamerv3-torch/assets/70328564/7f6e47a5-3235-4bc4-bef9-15ff96782d5e) ![dmcproprio](https://github.com/NM512/dreamerv3-torch/assets/70328564/7f6e47a5-3235-4bc4-bef9-15ff96782d5e)
#### DMC Vision
![dmcvision](https://github.com/NM512/dreamerv3-torch/assets/70328564/b710d217-2428-4fa0-8471-55e15ec5aa43)
#### Atari 100k
![atari100k](https://github.com/NM512/dreamerv3-torch/assets/70328564/0da6d899-d91d-44b4-a8c4-d5b37413aa11)
## Acknowledgments ## Acknowledgments
This code is heavily inspired by the following works: This code is heavily inspired by the following works: