Enhancing performance of reinforcement learning models in the presence of noisy rewards

Reinforcement learning models learn the optimal policy by interacting with the environment and observing the states and rewards. If the rewards that the model observes are noisy then learning an optimal policy becomes a difficult task. We present an approach that can enhance the performance of reinforcement learning models in the presence of noisy rewards. Along with the standard reinforcement learning model, we propose to use a noise filter which estimates the true reward that the model should receive. The noise filter is designed using a non-linear approximator. Through various experiments we demonstrate that this approach improves the performance of the model in the presence of noisy rewards