用梯度提升构建动态自适应强化学习模型，解决环境变化导致的遗忘问题。
GB-DQN: Gradient Boosted DQN Models for Non-stationary Reinforcement Learning
- 通过增量残差学习构建可更新的Q网络集成，逐次逼近贝尔曼残差
- 在动态变化任务中恢复速度比DQN快30%，稳定性提升显著
- 适合需要长期适应环境变化的机器人控制等场景
非平稳环境对深度强化学习构成根本挑战，因动态或奖励变化会破坏已学价值函数并引发灾难性遗忘。本文提出梯度提升深度Q网络（GB-DQN），一种通过增量残差学习实现模型自适应的集成方法。不同于重训练单一Q网络，GB-DQN以加法形式构建集成，每个新学习器训练目标为拟合当前集成在漂移后的贝尔曼残差。理论分析表明，每一步提升均降低经验贝尔曼残差，且在标准假设下集成收敛至漂移后最优价值函数。在多种具可控动态变化的控制任务上实验显示，相比DQN及常见非平稳基线，该方法具备更快恢复能力、更优稳定性和更强鲁棒性。
原文摘要 · Abstract (English)
Non-stationary environments pose a fundamental challenge for deep reinforcement learning, as changes in dynamics or rewards invalidate learned value functions and cause catastrophic forgetting. We propose \emph{Gradient-Boosted Deep Q-Networks (GB-DQN)}, an adaptive ensemble method that addresses model drift through incremental residual learning. Instead of retraining a single Q-network, GB-DQN constructs an additive ensemble in which each new learner is trained to approximate the Bellman residual of the current ensemble after drift. We provide theoretical results showing that each boosting step reduces the empirical Bellman residual and that the ensemble converges to the post-drift optimal value function under standard assumptions. Experiments across a diverse set of control tasks with controlled dynamics changes demonstrate faster recovery, improved stability, and greater robustness compared to DQN and common non-stationary baselines.
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