180 citations · 601 across the 46 of their papers we have counts for
13 papers · 1 filter
Improving the Efficiency of Off-Policy Reinforcement Learning by Accounting for Past Decisions
Brett Daley, Christopher Amato
Off-policy learning from multistep returns is crucial for sample-efficient reinforcement learning, particularly in the experience replay setting now commonly used with deep neural…
Virtual Replay Cache
Brett Daley, Christopher Amato
Return caching is a recent strategy that enables efficient minibatch training with multistep estimators (e.g. the λ-return) for deep reinforcement learning. By precomputing return…
Human-Level Control without Server-Grade Hardware
Brett Daley, Christopher Amato
Deep Q-Network (DQN) marked a major milestone for reinforcement learning, demonstrating for the first time that human-level control policies could be learned directly from raw visu…
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning
Yuchen Xiao, Xueguang Lyu, Christopher Amato
Policy gradient methods have become popular in multi-agent reinforcement learning, but they suffer from high variance due to the presence of environmental stochasticity and explori…
Investigating Alternatives to the Root Mean Square for Adaptive Gradient Methods
Brett Daley, Christopher Amato
Adam is an adaptive gradient method that has experienced widespread adoption due to its fast and reliable training performance. Recent approaches have not offered significant impro…
Reconciling Rewards with Predictive State Representations
Andrea Baisero, Christopher Amato
Predictive state representations (PSRs) are models of controlled non-Markov observation sequences which exhibit the same generative process governing POMDP observations without rel…