Alessandro Trapasso

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties. Markovian Reinforcement Learning (RL) can be inadequate for such tasks, while modeling them as non-Markovian reward decision processes (