TalkRL: The Reinforcement Learning Podcast
Abhishek Naik on Continuing RL & Average Reward
Feb 10, 2025
Abhishek Naik, a postdoctoral fellow at the National Research Council of Canada, recently completed his PhD in reinforcement learning under Rich Sutton. He explores average reward methods and their implications for continuous decision-making in AI. The discussion dives into innovative applications in space exploration and challenges in resource allocation, drawing on examples like Mars rovers. Abhishek emphasizes the transformative power of first-principles thinking, highlighting how AI advancements are shaping the future of spacecraft control and missions.
01:21:40
Episode guests
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- Average reward RL focuses on continuous decision-making, crucial for scenarios like resource allocation and autonomous taxi management.
- Real-world applications of average reward RL illustrate the need for autonomous systems, such as Mars rovers, to adapt in real-time decisions.
Deep dives
Understanding Average Reward RL
Average reward reinforcement learning (RL) is a mathematical formulation which differs from traditional methods by focusing on continuous decision-making scenarios. In this context, a continuing problem represents a situation where the agent interacts with the environment indefinitely, without any kind of time-outs or resets. The discussion highlights how average reward RL is particularly relevant for scenarios such as resource allocation on servers or autonomous taxi management, where decisions need to be made continuously based on evolving circumstances. This leads to complex challenges in decision-making that must factor in various parameters, such as request prioritization and future outcomes.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
