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Deep Residual Reinforcement Learning
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abstract
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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Cited by 2 Pith papers
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An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning
IDRL iteratively filters an offline dataset using learned visitation ratios and then runs weighted behavior cloning, outperforming several prior offline RL methods on D4RL and corrupted demonstrations.
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Touch begins where vision ends: Generalizable policies for contact-rich manipulation
A localize-then-execute policy that combines vision-language reaching, semantic background augmentation, and residual reinforcement learning with tactile sensing reaches about 90% success on millimeter-precision manip...
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