  Risk-bounded motion planning is an important yet difficult problem for
safety-critical tasks. While existing mathematical programming methods offer
theoretical guarantees in the context of constrained Markov decision processes,
they either lack scalability in solving larger problems or produce conservative
plans. Recent advances in deep reinforcement learning improve scalability by
learning policy networks as function approximators. In this paper, we propose
an extension of soft actor critic model to estimate the execution risk of a
plan through a risk critic and produce risk-bounded policies efficiently by
adding an extra risk term in the loss function of the policy network. We define
the execution risk in an accurate form, as opposed to approximating it through
a summation of immediate risks at each time step that leads to conservative
plans. Our proposed model is conditioned on a continuous spectrum of risk
bounds, allowing the user to adjust the risk-averse level of the agent on the
fly. Through a set of experiments, we show the advantage of our model in terms
of both computational time and plan quality, compared to a state-of-the-art
mathematical programming baseline, and validate its performance in more
complicated scenarios, including nonlinear dynamics and larger state space.
