Balancing Exploration and Exploitation in Self-imitation Learning

Abstract

Sparse reward tasks are always challenging in reinforcement learning. Learning such tasks requires both efficient exploitation and exploration to reduce the sample complexity. One line of research called self-imitation learning is recently proposed, which encourages the agent to do more exploitation by imitating past good trajectories. Exploration bonuses, however, is another line of research which enhances exploration by producing intrinsic reward when the agent visits novel states. In this paper, we introduce a novel framework Explore-then-Exploit (EE), which interleaves self-imitation learning with an exploration bonus to strengthen the effect of these two algorithms. In the exploring stage, with the aid of intrinsic reward, the agent tends to explore unseen states and occasionally collect high rewarding experiences, while in the self-imitating stage, the agent learns to consistently reproduce such experiences and thus provides a better starting point for subsequent stages. Our result shows that EE achieves superior or comparable performance on variants of MuJoCo environments with episodic reward settings.