94 citations · 161 across the 11 of their papers we have counts for
7 papers · 1 filter
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Siyi Hu, Yifan Zhong, Minquan Gao +6
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible d…
A2C is a special case of PPO
Shengyi Huang, Anssi Kanervisto, Antonin Raffin +3
Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years. A common understanding is t…
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
Jian Zhao, Youpeng Zhao, Weixun Wang +5
Multi-agent reinforcement learning is difficult to be applied in practice, which is partially due to the gap between the simulated and real-world scenarios. One reason for the gap…
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
Xiaotian Hao, Hangyu Mao, Weixun Wang +5
The state space in Multiagent Reinforcement Learning (MARL) grows exponentially with the agent number. Such a curse of dimensionality results in poor scalability and low sample eff…
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Yujing Hu, Weixun Wang, Hangtian Jia +5
Reward shaping is an effective technique for incorporating domain knowledge into reinforcement learning (RL). Existing approaches such as potential-based reward shaping normally ma…
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Tianpei Yang, Jianye Hao, Zhaopeng Meng +8
Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing tran…