Risk Sensitive Algorithm

Risk-sensitive algorithms in reinforcement learning aim to optimize decision-making processes by explicitly considering risk, rather than solely focusing on expected reward. Current research emphasizes developing and analyzing algorithms that incorporate various risk measures, such as Conditional Value-at-Risk (CVaR) and entropic risk measures, within established frameworks like Soft Actor-Critic and REINFORCE, often adapting them for specific problem structures like combinatorial optimization. This focus stems from the need for robust and reliable performance in high-stakes applications where unexpected outcomes can have significant consequences, leading to improved decision-making in domains ranging from operations research to online games. The field is actively exploring theoretical guarantees on convergence and regret, bridging the gap between theoretical analysis and practical efficacy.

Papers