Enabling the capability of assessing risk and making risk-aware decisions is
essential to applying reinforcement learning to safety-critical robots like
drones. In this paper, we investigate a specific case where a nano quadcopter
robot learns to navigate an apriori-unknown cluttered environment under partial
observability. We present a distributional reinforcement learning framework to
generate adaptive risk-tendency policies. Specifically, we propose to use lower
tail conditional variance of the learnt return distribution as intrinsic
uncertainty estimation, and use exponentially weighted average forecasting
(EWAF) to adapt the risk-tendency in accordance with the estimated uncertainty.
In simulation and real-world empirical results, we show that (1) the most
effective risk-tendency vary across states, (2) the agent with adaptive
risk-tendency achieves superior performance compared to risk-neutral policy or
risk-averse policy baselines.

研究如何使安全关键的机器人（如无人机）应用强化学习来评估风险并做出风险感知决策；通过提出分布式强化学习框架实现自适应风险倾向策略，在模拟环境和真实场景下发现最有效的风险倾向是各不相同的，并且自适应风险倾向的代理表现较好。