  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.
