updated README
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README.md
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README.md
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```
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## Benchmarks
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So far, this repository allows testing the following benchmarks.
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| Environment | Observation | Action | Description |
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|-------------------|---|---|-----------------------|
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| [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. |
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| [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. |
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| [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. |
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| [Crafter](https://github.com/danijar/crafter) | Image | Discrete | This survival environment evaluates diverse agent abilities, including exploration, reasoning, credit assignment, and generalization.|
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| [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.|
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So far, the following benchmarks can be used for testing.
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| Environment | Observation | Action | Budget | Description |
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|-------------------|---|---|---|-----------------------|
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| [DMC Proprio](https://github.com/deepmind/dm_control) | State | Continuous | 500K | DeepMind Control Suite with low-dimensional inputs. |
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| [DMC Vision](https://github.com/deepmind/dm_control) | Image | Continuous |1M| DeepMind Control Suite with high-dimensional images inputs. |
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| [Atari 100k](https://github.com/openai/atari-py) | Image | Discrete |400K| 26 Atari games. |
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| [Crafter](https://github.com/danijar/crafter) | Image | Discrete |1M| Survival environment to evaluates diverse agent abilities.|
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| [Memory Maze](https://github.com/jurgisp/memory-maze) | Image |Discrete |100M| 3D mazes to evaluate RL agents' long-term memory.|
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## Results
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#### DMC Vision
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#### Atari 100k
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#### DMC Proprio
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#### DMC Vision
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#### Atari 100k
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## Acknowledgments
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This code is heavily inspired by the following works:
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