Existing approaches for improving generalization in deep reinforcement
learning (RL) have mostly focused on representation learning, neglecting
RL-specific aspects such as exploration. We hypothesize that the agent's
exploration strategy plays a key role in its ability to generalize to new
environments. Through a series of experiments in a tabular contextual MDP, we
show that exploration is helpful not only for efficiently finding the optimal
policy for the training environments but also for acquiring knowledge that
helps decision making in unseen environments. Based on these observations, we
propose EDE: Exploration via Distributional Ensemble, a method that encourages
exploration of states with high epistemic uncertainty through an ensemble of
Q-value distributions. Our algorithm is the first value-based approach to
achieve state-of-the-art on both Procgen and Crafter, two benchmarks for
generalization in RL with high-dimensional observations. The open-sourced
implementation can be found at this https URL .

基于探索的深度强化学习方法对新环境具有良好的泛化能力，通过使用一种基于 Q 值分布集合的探索方法，该算法在 Procgen 和 Crafter 两个高维强化学习一般化基准上取得了最新的进展。