  In this paper, we model one-day international cricket games as Markov
processes, applying forward and inverse Reinforcement Learning (RL) to develop
three novel tools for the game. First, we apply Monte-Carlo learning to fit a
nonlinear approximation of the value function for each state of the game using
a score-based reward model. We show that, when used as a proxy for remaining
scoring resources, this approach outperforms the state-of-the-art
Duckworth-Lewis-Stern method used in professional matches by 3 to 10 fold.
Next, we use inverse reinforcement learning, specifically a variant of
guided-cost learning, to infer a linear model of rewards based on expert
performances, assumed here to be play sequences of winning teams. From this
model we explicitly determine the optimal policy for each state and find this
agrees with common intuitions about the game. Finally, we use the inferred
reward models to construct a game simulator that models the posterior
distribution of final scores under different policies. We envisage our
prediction and simulation techniques may provide a fairer alternative for
estimating final scores in interrupted games, while the inferred reward model
may provide useful insights for the professional game to optimize playing
strategy. Further, we anticipate our method of applying RL to this game may
have broader application to other sports with discrete states of play where
teams take turns, such as baseball and rounders.
