Expected Cumulative Reward
Expected Cumulative Reward is a concept in Reinforcement Learning that refers to the sum of rewards obtained by an agent over a sequence of time steps, taking into account the probability of each possible outcome. It is used to evaluate the performance of a policy, which is a mapping from states to actions. The expected cumulative reward is calculated by taking the expected value of the sum of rewards obtained by following the policy from a given starting state. This calculation takes into account the probability of transitioning from one state to another and the probability of receiving a reward at each time step. The goal of reinforcement learning is to find a policy that maximizes the expected cumulative reward.
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