39 citations · 39 across the 3 of their papers we have counts for
4 papers
Balancing Plasticity and Stability with Fast and Slow Successor Features
Raymond Chua, Doina Precup, Blake Richards
A hallmark of intelligence is the ability to adapt in non-stationary environments, yet deep Reinforcement Learning (RL) agents often struggle in such settings. Prior studies introd…
Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments
Riley Simmons-Edler, Ryan P. Badman, Felix Baastad Berg +5
Understanding the behavior of deep reinforcement learning (DRL) agents -particularly as task and agent sophistication increase- requires more than simple comparison of reward curve…
Learning Successor Features the Simple Way
Raymond Chua, Arna Ghosh, Christos Kaplanis +2
In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments. Successo…
Learning offline: memory replay in biological and artificial reinforcement learning
Emma L. Roscow, Raymond Chua, Rui Ponte Costa +2
Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning,…