50 citations · 163 across the 21 of their papers we have counts for
6 papers · 1 filter
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
Dongge Han, Wendelin Boehmer, Michael Wooldridge +1
In a multi-agent system, an agent's optimal policy will typically depend on the policies chosen by others. Therefore, a key issue in multi-agent systems research is that of predict…
Deep Coordination Graphs
Wendelin Böhmer, Vitaly Kurin, Shimon Whiteson
This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning. DCG strikes a flexible trade-off between representational capacity and…
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning
Wendelin Böhmer, Tabish Rashid, Shimon Whiteson
This paper investigates the use of intrinsic reward to guide exploration in multi-agent reinforcement learning. We discuss the challenges in applying intrinsic reward to multiple c…
Deep Residual Reinforcement Learning
Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algor…
Multitask Soft Option Learning
Maximilian Igl, Andrew Gambardella, Jinke He +4
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational…
Generalized Off-Policy Actor-Critic
Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
We propose a new objective, the counterfactual objective, unifying existing objectives for off-policy policy gradient algorithms in the continuing reinforcement learning (RL) setti…