Generative modeling of complex behaviors from labeled datasets has been a longstanding
problem in decision making. Unlike language or image generation, decision making …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

As previous representations for reinforcement learning cannot effectively incorporate a
human-intuitive understanding of the 3D environment, they usually suffer from sub-optimal …

Abstract Recent curriculum Reinforcement Learning (RL) has shown notable progress in
solving complex tasks by proposing sequences of surrogate tasks. However, the previous …

Modularity is a general principle present in many fields. It offers attractive advantages,
including, among others, ease of conceptualization, interpretability, scalability, module …

Latent scene representation plays a significant role in training reinforcement learning (RL)
agents. To obtain good latent vectors describing the scenes, recent works incorporate the …

Offline Reinforcement Learning (RL) has emerged as a powerful alternative to imitation
learning for behavior modeling in various domains, particularly in complex navigation tasks …

Recent studies reveal that well-performing reinforcement learning (RL) agents in training
often lack resilience against adversarial perturbations during deployment. This highlights the …

In cooperative multi-agent reinforcement learning (MARL), agents collaborate to achieve
common goals, such as defeating enemies and scoring a goal. However, learning goal …

Decomposing complex real-world tasks into simpler subtasks and devising a subtask
execution plan is critical for humans to achieve effective decision-making. However …