  Risk-sensitive reinforcement learning (RL) has garnered significant attention
in recent years due to the growing interest in deploying RL agents in
real-world scenarios. A critical aspect of risk awareness involves modeling
highly rare risk events (rewards) that could potentially lead to catastrophic
outcomes. These infrequent occurrences present a formidable challenge for
data-driven methods aiming to capture such risky events accurately. While
risk-aware RL techniques do exist, their level of risk aversion heavily relies
on the precision of the state-action value function estimation when modeling
these rare occurrences. Our work proposes to enhance the resilience of RL
agents when faced with very rare and risky events by focusing on refining the
predictions of the extreme values predicted by the state-action value function
distribution. To achieve this, we formulate the extreme values of the
state-action value function distribution as parameterized distributions,
drawing inspiration from the principles of extreme value theory (EVT). This
approach effectively addresses the issue of infrequent occurrence by leveraging
EVT-based parameterization. Importantly, we theoretically demonstrate the
advantages of employing these parameterized distributions in contrast to other
risk-averse algorithms. Our evaluations show that the proposed method
outperforms other risk averse RL algorithms on a diverse range of benchmark
tasks, each encompassing distinct risk scenarios.
