Deep Residual Reinforcement Learning

Deep Residual Reinforcement Learning

연구 분야: Artificial Intelligence

학회: AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.