Exploration strategies in deep reinforcement learning environments

This technical article addresses the exploration-exploitation tradeoff fundamental to reinforcement learning, examining strategies for discovering optimal solutions efficiently while avoiding premature commitment to suboptimal policies. The survey covers classic bandit algorithms and their application to deep reinforcement learning with neural network function approximation. The article discusses two critical challenge scenarios: hard-exploration problems with sparse or deceptive rewards, and the noisy-TV problem where agents optimizing for novelty may pursue meaningless stimuli. Specific benchmarks like Montezuma's Revenge exemplify environments requiring sophisticated exploration. Various modern exploration techniques are presented to address these scenarios, acknowledging that exploration remains an open research problem in deep RL where no single approach dominates across all problem classes.