Meta Reinforcement Learning with Successor Feature Based Context

2022-07-29 · Xu Han, Feng Wu

Most reinforcement learning (RL) methods only focus on learning a single task from scratch and are not able to use prior knowledge to learn other tasks more effectively. Context-based meta RL techniques are recently proposed as a possible solution to tackle this. However, they are usually less efficient than conventional RL and may require many trial-and-errors during training. To address this, we propose a novel meta-RL approach that achieves competitive performance comparing to existing meta-RL algorithms, while requires significantly fewer environmental interactions. By combining context variables with the idea of decomposing reward in successor feature framework, our method does not only learn high-quality policies for multiple tasks simultaneously but also can quickly adapt to new tasks with a small amount of training. Compared with state-of-the-art meta-RL baselines, we empirically show the effectiveness and data efficiency of our method on several continuous control tasks.

📄 PDF Abstract BibTeX arXiv:2207.14723

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continuous-control Continuous ControlMeta Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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