Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the
transformer architecture in reinforcement learning (RL). However, a notable limitation of DT …

Pre-training on task-agnostic large datasets is a promising approach for enhancing the
sample efficiency of reinforcement learning (RL) in solving complex tasks. We present …

Decision-making is a dynamic process requiring perception, memory, and reasoning to
make choices and find optimal policies. Traditional approaches to decision-making suffer …

While the conditional sequence modeling with the transformer architecture has
demonstrated its effectiveness in dealing with offline reinforcement learning (RL) tasks, it is …

In this paper, we consider the supervised pretrained transformer for a class of sequential
decision-making problems. The class of considered problems is a subset of the general …

Decision Transformers (DTs) have been highly effective for offline reinforcement learning
(RL) tasks, successfully modeling the sequences of actions in a given set of demonstrations …

Despite the promising performance of Decision Transformers (DT) on a wide range of tasks,
recent studies have found that the performance of DT may largely be dependent on the …

The Theory of Mind (ToM) ability in multi-agent systems is crucial for coordinating
cooperation and understanding communication. ToM involves the capacity to reason about …

Sequence models have emerged as an alternate paradigm for offline Reinforcement
Learning (RL) with their remarkable generative capabilities. However, it struggles in cases …