180 citations · 601 across the 46 of their papers we have counts for
20 papers · 1 filter
Improving Deep Policy Gradients with Value Function Search
Enrico Marchesini, Christopher Amato
Deep Policy Gradient (PG) algorithms employ value networks to drive the learning of parameterized policies and reduce the variance of the gradient estimates. However, value functio…
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Brett Daley, Martha White, Christopher Amato +1
Off-policy learning from multistep returns is crucial for sample-efficient reinforcement learning, but counteracting off-policy bias without exacerbating variance is challenging. C…
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Yuchen Xiao, Weihao Tan, Christopher Amato
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination…
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
Kevin Esslinger, Robert Platt, Christopher Amato
Real-world reinforcement learning tasks often involve some form of partial observability where the observations only give a partial or noisy view of the true state of the world. Su…
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
Sammie Katt, Hai Nguyen, Frans A. Oliehoek +1
While reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) pro…
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
Xueguang Lyu, Andrea Baisero, Yuchen Xiao +1
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learn…