Wenjia Meng

Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network

2019 · IEEE Transactions on Neural Networks and Learning Systems

The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. The DQN brings advances to complex sequential decision problems, while return-based algorithms have advantages in making use of sample …

An Off-Policy Trust Region Policy Optimization Method With Monotonic Improvement Guarantee for Deep Reinforcement Learning

2021 · IEEE Transactions on Neural Networks and Learning Systems

In deep reinforcement learning, off-policy data help reduce on-policy interaction with the environment, and the trust region policy optimization (TRPO) method is efficient to stabilize the policy optimization procedure. In this article, we propose an …

MTRL-CG: Multi-Task Reinforcement Learning Method with Spectral Clustering-Based Task Grouping

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Multi-task reinforcement learning (RL) aims to enhance agent performance across multiple tasks by enabling effective knowledge transfer. However, these methods adopt a fully shared policy across all tasks without explicitly distinguishing between related and conflicting …