An actor-critic algorithm with a Monte Carlo risk critic is proposed for optimizing reinforcement learning policies under arbitrary, possibly non-coherent risk measures, with a risk function fitted from simulated data.
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Practical Risk Measures in Reinforcement Learning
An actor-critic algorithm with a Monte Carlo risk critic is proposed for optimizing reinforcement learning policies under arbitrary, possibly non-coherent risk measures, with a risk function fitted from simulated data.