状態行動分布に基づくゴールサンプリングによる自動カリキュラム学習

抄録

It is mostly challenging to implement reinforcement learning due to vast search space. To address this issue, Zhang et al. proposed Value Disagreement Sampling (VDS), which sets pseudo-goals based on degree of disagreement within the multiple value functions. However, the VDS approach may not set contributory pseudo-goals to learn task objectives. In this paper, we aim to enhance the learning efficiency by sampling pseudo-goals based on the state-action distribution sampled from current policy. Simulation results demonstrate the effectiveness of the proposed approach in improving learning efficiency, especially during the later stages of the learning process.