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AI Research
arXiv CS.AI·Jun 9·5 min read
Position: Deployed Reinforcement Learning should be Continual
TL;DR
Reinforcement Learning (RL) systems often follow a train-then-fix approach, leading to inefficiencies as they do not learn during deployment. This position paper argues for a continual RL framework, where agents learn from ongoing interactions and evaluative rewards.
✦ Why It Matters
Engineers can implement continual learning strategies in RL systems to enhance adaptability and performance in changing environments.
Key Takeaways
- Adopt continual learning for deployed RL agents.
- Identify sources of non-stationarity in your environment.
- Analyze successful continual RL examples for insights.
- Shift away from the train-then-fix approach.
How It Works
Continual learning in RL involves agents that continuously update their knowledge and strategies based on real-time feedback from their environment. This approach allows agents to adapt to changes, such as shifts in user behavior or environmental conditions, ensuring they remain effective over time.