Ruixuan Miao

T4NMTD: Transition-Centric Reinforcement Learning for Non-Markovian Task Decomposition

Non-Markovian Tasks (NMTs) are distinguished by their dependence on long-term memory and state-dependent dynamics, setting them apart from the traditional Markovian models typically employed in Reinforcement Learning (RL). NMTs not only suffer from reward sparseness but also rely on historical infor