  Stochastic optimal control of dynamical systems is a crucial challenge in
sequential decision-making. Recently, control-as-inference approaches have had
considerable success, providing a viable risk-sensitive framework to address
the exploration-exploitation dilemma. Nonetheless, a majority of these
techniques only invoke the inference-control duality to derive a modified risk
objective that is then addressed within a reinforcement learning framework.
This paper introduces a novel perspective by framing risk-sensitive stochastic
control as Markovian score climbing under samples drawn from a conditional
particle filter. Our approach, while purely inference-centric, provides
asymptotically unbiased estimates for gradient-based policy optimization with
optimal importance weighting and no explicit value function learning. To
validate our methodology, we apply it to the task of learning neural
non-Gaussian feedback policies, showcasing its efficacy on numerical benchmarks
of stochastic dynamical systems.
