Papers › Deep Residual Reinforcement Learning

Deep Residual Reinforcement Learning

3 May 2019arXiv:1905.01072archive 2025-07-28

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

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.

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ShangtongZhang/DeepRL officialmentioned in papermentioned on GitHubpytorchMIT report

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Model-based Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Adam Batch Normalization Convolution DDPG Dense Connections Experience Replay ReLU Weight Decay

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