用惩罚函数+双向学习，让智能体学会避错并更快适应复杂环境
Enhanced Penalty-based Bidirectional Reinforcement Learning Algorithms
- 引入惩罚函数引导智能体避开错误动作，结合正向与反向状态学习
- 在Mani技能基准上成功率提升约4%，优化了策略学习效率
- 适合需要高鲁棒性和快速适应的复杂控制任务研究者参考
本研究通过引入惩罚函数，增强强化学习算法对智能体的引导能力，使其不仅学习最优动作，还明确哪些行为应避免。同时，重新提出双向学习机制，使智能体能从初始状态和终止状态中共同学习，显著提升在复杂环境中的训练速度与鲁棒性。所提出的基于惩罚的双向学习方法在Mani技能基准环境中进行测试，相较于现有RL实现，成功率提升了约4%。结果表明，该整合策略有效增强了策略学习能力、适应性及整体性能，尤其适用于高挑战性场景。
原文摘要 · Abstract (English)
This research focuses on enhancing reinforcement learning (RL) algorithms by integrating penalty functions to guide agents in avoiding unwanted actions while optimizing rewards. The goal is to improve the learning process by ensuring that agents learn not only suitable actions but also which actions to avoid. Additionally, we reintroduce a bidirectional learning approach that enables agents to learn from both initial and terminal states, thereby improving speed and robustness in complex environments. Our proposed Penalty-Based Bidirectional methodology is tested against Mani skill benchmark environments, demonstrating an optimality improvement of success rate of approximately 4% compared to existing RL implementations. The findings indicate that this integrated strategy enhances policy learning, adaptability, and overall performance in challenging scenarios
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