Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL
arXiv:2606.25012v1 Announce Type: new Abstract: Many reinforcement learning (RL) problems in the infinite-horizon average-reward setting require optimizing multiple conflicting objectives while satisfying multiple safety constraints. A common approach is concave scalarization, where the agent maximizes a utility $ f(J^\pi_{r_1}, \ldots, J^\pi_{r_M}) $ subject to a scalarized constraint $ g(J^\pi_{c_1}, \ldots, J^\pi_{c_N}) \ge 0 $, where $J^\pi_{r_m}$ and $J^\pi_{c_n}$ denote the average-reward and cost under policy $\pi$. However, the nonlinearity of $f$ and $g$ introduces bias in policy-grad
The increasing complexity and real-world application of AI demand more sophisticated reinforcement learning techniques to handle multi-objective optimization and safety constraints effectively.
This research provides a foundational improvement to reinforcement learning algorithms, directly enhancing AI's capability to operate robustly and safely in complex, constrained environments.
Theoretically, this advancement allows for the development of more reliable and 'bias-controlled' AI agents capable of balancing multiple, potentially conflicting, goals with strong safety guarantees.
- · AI algorithm developers
- · Robotics
- · Autonomous systems
- · AI research institutions
- · Developers using less sophisticated RL frameworks
- · Existing suboptimal multi-objective RL solutions
Improved performance and safety in complex AI applications like autonomous vehicles and industrial control systems.
Faster adoption of AI in safety-critical domains due to enhanced reliability and predictability.
Increased public trust and regulatory acceptance of AI operating in environments with significant real-world consequences.
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Read at arXiv cs.LG