Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approach
<|reference_start|>Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approach: As the complexity of tasks addressed through reinforcement learning (RL) increases, the definition of reward functions also has become highly complicated. We introduce an RL method aimed at simplifying the reward-shaping process through intuitive strategies. Initially, instead of a single reward function composed of various terms, we define multiple reward and cost functions within a constrained multi-objective RL (CMORL) framework. For tasks involving sequential complex movements, we segment the task into distinct stages and define multiple rewards and costs for each stage. Finally, we introduce a practical CMORL algorithm that maximizes objectives based on these rewards while satisfying constraints defined by the costs. The proposed method has been successfully demonstrated across a variety of acrobatic tasks in both simulation and real-world environments. Additionally, it has been shown to successfully perform tasks compared to existing RL and constrained RL algorithms. Our code is available at https://github.com/rllab-snu/Stage-Wise-CMORL.<|reference_end|>
@article{kim2024stage-wise,
    title={Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained
  Multi-Objective Reinforcement Learning Approach},
    author={Dohyeong Kim, Hyeokjin Kwon, Junseok Kim, Gunmin Lee, Songhwai Oh},
    journal={arXiv preprint arXiv:2409.15755},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15755},
    primaryClass={cs.RO cs.AI}
}