Curriculum goal masking for continuous deep reinforcement learning

Deep reinforcement learning has recently gained a focus on problems where policy or value functions are based on universal value function approximators (UVFAs) which renders them independent of goals. Evidence exists that the sampling of goals has a strong effect on the learning performance, and the problem of optimizing the goal sampling is frequently tackled with intrinsic motivation methods. However, there is a lack of general mechanisms that focus on goal sampling in the context of deep reinforcement learning based on UVFAs. In this work, we introduce goal masking as a method to estimate a goal's difficulty level and to exploit this estimation to realize curriculum learning. Our results indicate that focusing on goals with a medium difficulty level is appropriate for deep deterministic policy gradient (DDPG) methods, while an “aim for the stars and reach the moon-strategy”, where difficult goals are sampled much more often than simple goals, leads to the best learning performance in cases where DDPG is combined with hindsight experience replay (HER).