Machine Learning, Mdp, Markov Chains, Reinforcement Learning.
that we take action a in state s. In a deterministic world, these values will equal 1 because we’re 100% sure that we will end up where we expect, given an action. We can configure this when modelling the environment. Rewards We use some notion of reward to help the agent take the next best step.