1.1k citations · 5k across the 117 of their papers we have counts for
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TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
Gregory Farquhar, Tim Rocktäschel, Maximilian Igl +1
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has p…
Learning with Opponent-Learning Awareness
Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat +3
Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be exten…
Expected Policy Gradients
Kamil Ciosek, Shimon Whiteson
We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected…
Counterfactual Multi-Agent Policy Gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras +2
Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a grea…
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar +4
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing mul…