  Though deep reinforcement learning (DRL) has obtained substantial success, it
may encounter catastrophic failures due to the intrinsic uncertainty of both
transition and observation. Most of the existing methods for safe reinforcement
learning can only handle transition disturbance or observation disturbance
since these two kinds of disturbance affect different parts of the agent;
besides, the popular worst-case return may lead to overly pessimistic policies.
To address these issues, we first theoretically prove that the performance
degradation under transition disturbance and observation disturbance depends on
a novel metric of Value Function Range (VFR), which corresponds to the gap in
the value function between the best state and the worst state. Based on the
analysis, we adopt conditional value-at-risk (CVaR) as an assessment of risk
and propose a novel reinforcement learning algorithm of
CVaR-Proximal-Policy-Optimization (CPPO) which formalizes the risk-sensitive
constrained optimization problem by keeping its CVaR under a given threshold.
Experimental results show that CPPO achieves a higher cumulative reward and is
more robust against both observation and transition disturbances on a series of
continuous control tasks in MuJoCo.
