Exploration Strategies in Deep Reinforcement Learning
Read OriginalThis technical article examines the critical challenge of exploration vs. exploitation in Deep Reinforcement Learning (DRL). It details classic strategies like epsilon-greedy and UCB, then discusses modern DRL approaches such as entropy regularization and noise-based exploration. The article also analyzes specific exploration problems like the 'hard-exploration' issue (e.g., in Montezuma's Revenge) and the 'Noisy-TV' problem, positioning it as a resource for understanding and improving agent exploration in complex environments.
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