arXiv:2605.26357v1 Announce Type: new Abstract: 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 MuJ
The research addresses a fundamental challenge (plasticity-stability dilemma) in deep RL, which is amplified as AI applications move into more dynamic and real-world environments.
Improving RL agents' ability to adapt to non-stationary environments is crucial for their deployment in complex, continuously evolving real-world scenarios, particularly autonomous systems and AI agents.
This paper offers a novel approach to making RL agents more robust and adaptable to gradual environmental changes, suggesting a path to more reliable and generalizable AI.
- · AI agents developers
- · Robotics industry
- · Deep Reinforcement Learning researchers
- · Any industry using autonomous systems
- · RL methods susceptible to catastrophic forgetting
- · Deep Reinforcement Learning approaches lacking adaptability
RL agents will be able to perform effectively in environments with continuous, gradual changes.
The improved adaptability of RL agents could accelerate the development and deployment of more sophisticated autonomous systems.
More robust and adaptable AI agents could lead to significant advancements in areas requiring continuous learning and environmental interaction, potentially collapsing workflows.
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