arXiv:2606.19134v1 Announce Type: cross Abstract: We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a set of reward machines (RMs). PQLRM combines Pareto Q-Learning (PQL), which maintains sets of vector-valued Q-estimates to approximate the Pareto front, with enhancements from Q-Learning with Reward Machines (QRM), which exploits the factored automaton structure of the reward signal. This yields a multi-policy algorithm that remains sample-efficient under non-Markovian, RM-encoded rewar
The continuous evolution of AI research seeks more efficient and robust reinforcement learning methods for increasingly complex, real-world applications.
This development allows AI agents to learn and optimize for multiple competing objectives simultaneously, enhancing their applicability in sophisticated autonomous systems.
AI systems can now better handle complex reward structures and non-Markovian environments, leading to more nuanced and effective decision-making in multi-objective scenarios.
- · AI/ML researchers
- · Robotics industry
- · Autonomous systems developers
- · Traditional single-objective reinforcement learning approaches
Improved performance and broader application of multi-objective reinforcement learning agents.
Accelerated development of more adaptive and intelligent autonomous systems capable of balancing trade-offs.
Enhanced AI capabilities contributing to the development of sophisticated autonomous agents that can perform complex white-collar tasks.
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Read at arXiv cs.AI