360 citations · 2.7k across the 150 of their papers we have counts for
156 papers
Policy composition in reinforcement learning via multi-objective policy optimization
Shruti Mishra, Ankit Anand, Jordan Hoffmann +4
We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teacher policies are introduced as objectives…
On the Convergence of Bounded Agents
David Abel, André Barreto, Hado van Hasselt +3
When has an agent converged? Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence: An agent converges when its behavior or…
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets
Nikhil Vemgal, Elaine Lau, Doina Precup
Reinforcement Learning (RL) algorithms aim to learn an optimal policy by iteratively sampling actions to learn how to maximize the total expected return, . GFlowNets are a sp…
A Definition of Continual Reinforcement Learning
David Abel, André Barreto, Benjamin Van Roy +3
In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on…
Acceleration in Policy Optimization
Veronica Chelu, Tom Zahavy, Arthur Guez +2
We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimist…
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Scott Fujimoto, Wei-Di Chang, Edward J. Smith +3
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states,…