Exploration Strategies in Deep Reinforcement Learning

Lilian Weng · 6 years ago

This article discusses exploration strategies in deep reinforcement learning, focusing on how RL agents balance finding optimal solutions quickly while avoiding premature commitment to suboptimal policies. The article covers classic exploration methods like epsilon-greedy and Boltzmann exploration, then details modern deep RL approaches including count-based exploration using density models and hashing techniques with SimHash for high-dimensional states. The strategies aim to address hard-exploration problems in sparse reward environments and the noisy-TV problem where agents become distracted by irrelevant novel experiences.