812 citations · 825 across the 8 of their papers we have counts for
8 papers · 1 filter
Should Models Be Accurate?
Esra'a Saleh, John D. Martin, Anna Koop +2
Model-based Reinforcement Learning (MBRL) holds promise for data-efficiency by planning with model-generated experience in addition to learning with experience from the environment…
Useful Policy Invariant Shaping from Arbitrary Advice
Paniz Behboudian, Yash Satsangi, Matthew E. Taylor +2
Reinforcement learning is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in com…
Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task
Katya Kudashkina, Valliappa Chockalingam, Graham W. Taylor +1
Human-computer interactive systems that rely on machine learning are becoming paramount to the lives of millions of people who use digital assistants on a daily basis. Yet, further…
The Hanabi Challenge: A New Frontier for AI Research
Nolan Bard, Jakob N. Foerster, Sarath Chandar +12
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made…
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi +4
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algo…
Generalization and Regularization in DQN
Jesse Farebrother, Marlos C. Machado, Michael Bowling
Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performanc…