  We propose a framework for applying reinforcement learning to contextual
two-stage stochastic optimization and apply this framework to the problem of
energy market bidding of an off-shore wind farm. Reinforcement learning could
potentially be used to learn close to optimal solutions for first stage
variables of a two-stage stochastic program under different contexts. Under the
proposed framework, these solutions would be learned without having to solve
the full two-stage stochastic program. We present initial results of training
using the DDPG algorithm and present intended future steps to improve
performance.
