With extensive pretrained knowledge and high-level general capabilities, large language
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …

Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-
making by learning through interaction with their environment. Central to this process are …

Self evolution refers to the ability of a system to evolve autonomously towards a better
performance, which is a potential trend for autonomous driving systems based on self …

Efficiently searching for novel molecules with specific properties is critical to molecular
generation. Some existing works focus on combining deep generative models and …

Existing multiagent exploration works focus on how to explore in the fully cooperative task,
which is insufficient in the environment with nonstationarity induced by agent interactions. To …

This paper proposes a fully decentralized approach to address the challenge of general
mixed cooperation and competition within the domain of Multi-Agent Reinforcement …

Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning
problems. Although population-based learning shows its potential in producing diverse …

As machine learning models become more capable, they have exhibited increased potential
in solving complex tasks. One of the most promising directions uses deep reinforcement …

Reinforcement learning (RL) can align language models with non-differentiable reward
signals, such as human preferences. However, a major challenge arises from the sparsity of …