NUPAR: A Benchmark Suite for Modern GPU Architectures. - Yash Ukidave; Fanny Nina Paravecino; Leiming Yu; Charu Kalra; Amir Momeni; Zhongliang Chen; Nick Materise; Brett Daley; Perhaad Mistry; David R. Kaeli (ICPE, 2015)

Efficient Eligibility Traces for Deep Reinforcement Learning. - Brett Daley; Christopher Amato (CoRR, 2018)

Reconciling λ-Returns with Experience Replay. - Brett Daley; Christopher Amato (NeurIPS, 2019)

Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability. - Hai Nguyen 001; Brett Daley; Xinchao Song 001; Christopher Amato; Robert Platt 001 (CoRL, 2020)

Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties. - Brett Daley; Christopher Amato (CoRR, 2020)

Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning. - Brett Daley; Cameron Hickert; Christopher Amato (AAMAS, 2021)

Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning. - Xueguang Lyu; Yuchen Xiao; Brett Daley; Christopher Amato (AAMAS, 2021)

Investigating Alternatives to the Root Mean Square for Adaptive Gradient Methods. - Brett Daley; Christopher Amato (CoRR, 2021)

Human-Level Control without Server-Grade Hardware. - Brett Daley; Christopher Amato (CoRR, 2021)

Virtual Replay Cache. - Brett Daley; Christopher Amato (CoRR, 2021)

Improving the Efficiency of Off-Policy Reinforcement Learning by Accounting for Past Decisions. - Brett Daley; Christopher Amato (CoRR, 2021)

Asymmetric DQN for partially observable reinforcement learning. - Andrea Baisero; Brett Daley; Christopher Amato (UAI, 2022)

Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning. - Brett Daley; Isaac Chan (CoRR, 2022)

Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. - Brett Daley; Martha White; Christopher Amato; Marlos C. Machado (ICML, 2023)

On Centralized Critics in Multi-Agent Reinforcement Learning. - Xueguang Lyu; Andrea Baisero; Yuchen Xiao; Brett Daley; Christopher Amato (J. Artif. Intell. Res., 2023)

Averaging n-step Returns Reduces Variance in Reinforcement Learning. - Brett Daley; Martha White; Marlos C. Machado (ICML, 2024)

Demystifying the Recency Heuristic in Temporal-Difference Learning. - Brett Daley; Marlos C. Machado; Martha White (RLJ, 2024)

Compound Returns Reduce Variance in Reinforcement Learning. - Brett Daley; Martha White; Marlos C. Machado (CoRR, 2024)

Deep Reinforcement Learning with Gradient Eligibility Traces. - Esraa Elelimy; Brett Daley; Andrew Patterson; Marlos C. Machado; Adam White 001; Martha White (CoRR, 2025)

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values. - Brett Daley; Prabhat Nagarajan; Martha White; Marlos C. Machado (CoRR, 2025)