Continual Bench provides an environment suitable for evaluating online reinforcement learning agents under the continual learning setup, with a unified world dynamics.
git clone git@github.com:sail-sg/ContinualBench.git && cd ContinualBench
pip install -e .
from continual_bench.envs import ContinualBenchEnv
env = ContinualBenchEnv(render_mode="rgb_array", seed=0)
action = ...
next_obs, reward, terminated, truncated, info = env.step(action)
continual-bench
is distributed under the terms of the MIT license.
If you find our work useful for your research, please consider citing:
@inproceedings{liu2025continual,
title={Continual Reinforcement Learning by Planning with Online World Models},
author={Liu, Zichen and Fu, Guoji and Du, Chao and Lee, Wee Sun and Lin, Min},
booktitle={International Conference on Machine Learning},
year={2025}
}