Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Multi-agent systems are characterized by environmental uncertainty, varying policies of
agents, and partial observability, which result in significant risks. In the context of Multi-Agent …

Distributional reinforcement learning algorithms have attempted to utilize estimated
uncertainty for exploration, such as optimism in the face of uncertainty. However, using the …

In autonomous driving, reinforcement learning's interactive learning capabilities have
garnered interest. Among these, risk-averse distributional reinforcement learning has …

Distributional Reinforcement Learning (RL) estimates return distribution mainly by learning
quantile values via minimizing the quantile Huber loss function, entailing a threshold …

Multi-Agent Reinforcement Learning (MARL) has gained significant traction for solving
complex real-world tasks, but the inherent stochasticity and uncertainty in these …

The use of reinforcement learning (RL) in practical applications requires considering sub-
optimal outcomes, which depend on the agent's familiarity with the uncertain environment …

In cultural knowledge evolution simulated by multi-agent simulations, agents can improve
the accuracy of their knowledge by interacting with other agents and adapting their …

The advent of reinforcement learning (RL) has significantly transformed decision making in
autonomous systems. However, its practical deployment faces substantial obstacles, chiefly …

One of the main challenges in reinforcement learning (RL) is that the agent has to make
decisions that would influence the future performance without having complete knowledge …