47 citations · 74 across the 7 of their papers we have counts for
7 papers
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training
Pihe Hu, Shaolong Li, Zhuoran Li +2
Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous parameters in multi-agent scenarios, often incurring substantial computational overhead. Cons…
One is More: Diverse Perspectives within a Single Network for Efficient DRL
Yiqin Tan, Ling Pan, Longbo Huang
Deep reinforcement learning has achieved remarkable performance in various domains by leveraging deep neural networks for approximating value functions and policies. However, using…
Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL
Zhuoran Li, Ling Pan, Jiatai Huang +1
We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL). Different from existing algorithms that rely mainly on conserva…
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance
Can Chang, Ni Mu, Jiajun Wu +2
A critical challenge in multi-agent reinforcement learning(MARL) is for multiple agents to efficiently accomplish complex, long-horizon tasks. The agents often have difficulties in…
Softmax Deep Double Deterministic Policy Gradients
Ling Pan, Qingpeng Cai, Longbo Huang
A widely-used actor-critic reinforcement learning algorithm for continuous control, Deep Deterministic Policy Gradients (DDPG), suffers from the overestimation problem, which can n…
Deterministic Policy Gradients With General State Transitions
Qingpeng Cai, Ling Pan, Pingzhong Tang
We study a reinforcement learning setting, where the state transition function is a convex combination of a stochastic continuous function and a deterministic function. Such a sett…