Balancing Plasticity and Stability with Fast and Slow Successor Features
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 introduce non-stationarity through abrupt shifts in features or dynamics, whereas real-world environments often evolve gradually through continual drift. This distinction has important implications for the "stability-plasticity dilemma" in RL, as abrupt task changes may demand more plasticity than naturalistic settings. To address this, we modify existing 3D Miniworld and MuJoCo environments to incorporate naturalistic, continual non-stationarity, and use them to examine how stability and adaptation affect performance...
| Authors | Raymond Chua, Doina Precup, Blake Richards |
|---|---|
| Primary category | cs.LG |
| Published | 25 May 2026 |
| Last updated at source | 27 May 2026 |
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