Self-supervised network distillation: an effective approach to exploration in sparse reward environments
This addresses exploration challenges in reinforcement learning for agents in sparse reward settings, representing an incremental improvement over existing novelty detection methods.
The paper tackled the problem of sparse reward environments in reinforcement learning by introducing Self-supervised Network Distillation (SND), an intrinsic motivation method based on distillation error for novelty detection, which achieved faster growth and higher external reward compared to baselines in ten difficult environments.
Reinforcement learning can solve decision-making problems and train an agent to behave in an environment according to a predesigned reward function. However, such an approach becomes very problematic if the reward is too sparse and so the agent does not come across the reward during the environmental exploration. The solution to such a problem may be to equip the agent with an intrinsic motivation that will provide informed exploration during which the agent is likely to also encounter external reward. Novelty detection is one of the promising branches of intrinsic motivation research. We present Self-supervised Network Distillation (SND), a class of intrinsic motivation algorithms based on the distillation error as a novelty indicator, where the predictor model and the target model are both trained. We adapted three existing self-supervised methods for this purpose and experimentally tested them on a set of ten environments that are considered difficult to explore. The results show that our approach achieves faster growth and higher external reward for the same training time compared to the baseline models, which implies improved exploration in a very sparse reward environment. In addition, the analytical methods we applied provide valuable explanatory insights into our proposed models.