Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded
datasets without online environment interaction. It is unfortunate that extrapolation errors can …

Goal-conditioned reinforcement learning (RL) with sparse rewards remains a challenging
problem in deep RL. Hindsight Experience Replay (HER) has been demonstrated to be an …

The prevalent use of benchmarks in current offline reinforcement learning (RL) research has
led to a neglect of the imbalance of real-world dataset distributions in the development of …

Among various branches of offline reinforcement learning (RL) methods, goal-conditioned
supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL …

This paper examines the use of classical deep reinforcement learning (DRL) algorithms,
DQN, DDQN, and Dueling DQN, in the strategy game So Long Sucker (SLS), a diplomacy …