Deep residual reinforcement learning
<p>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...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
International Foundation for Autonomous Agents and Multiagent Systems
2020
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