Efficient Off-Policy Safe Reinforcement Learning Using Trust Region
  Conditional Value at Risk
Dohyeong Kim Songhwai Oh
This paper aims to solve a safe reinforcement learning (RL) problem with risk
measure-based constraints. As risk measures, such as conditional value at risk
(CVaR), focus on the tail distribution of cost signals, constraining risk
measures can effectively prevent a failure in the worst case. An on-policy safe
RL method, called TRC, deals with a CVaR-constrained RL problem using a trust
region method and can generate policies with almost zero constraint violations
with high returns. However, to achieve outstanding performance in complex
environments and satisfy safety constraints quickly, RL methods are required to
be sample efficient. To this end, we propose an off-policy safe RL method with
CVaR constraints, called off-policy TRC. If off-policy data from replay buffers
is directly used to train TRC, the estimation error caused by the
distributional shift results in performance degradation. To resolve this issue,
we propose novel surrogate functions, in which the effect of the distributional
shift can be reduced, and introduce an adaptive trust-region constraint to
ensure a policy not to deviate far from replay buffers. The proposed method has
been evaluated in simulation and real-world environments and satisfied safety
constraints within a few steps while achieving high returns even in complex
robotic tasks.