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.