While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

A major challenge in reinforcement learning is to determine which state-action pairs are
responsible for future rewards that are delayed. Reward redistribution serves as a solution to …

In many reinforcement learning tasks, the agent has to learn to interact with many objects of
different types and generalize to unseen combinations and numbers of objects. Often a task …

The varying significance of distinct primitive behaviors during the policy learning process
has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore …

One of the key challenges in deploying RL to real-world applications is to adapt to variations
of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated …

Cooperative multi-agent reinforcement learning (MARL), where agents coordinates with
teammate (s) for a shared goal, may sustain non-stationary caused by the policy change of …

In the sequential decision making setting, an agent aims to achieve systematic
generalization over a large, possibly infinite, set of environments. Such environments are …

We first raise and tackle a``time synchronization''issue between the agent and the
environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real …

Reinforcement learning (RL) is a powerful technique that allows an autonomous agent to
learn an optimal policy to maximize the expected return. The optimality of various RL …

Abstract Causal Temporal Representation Learning (Ctrl) methods aim to identify the
temporal causal dynamics of complex nonstationary temporal sequences. Despite the …