Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 12 | Summary and Q&A

TL;DR

Meta RL explores the challenges of end-to-end optimization for exploration strategies and proposes alternative approaches to decouple exploration from execution.

Key Insights

• 🛄 Meta RL aims to solve the challenge of exploration in reinforcement learning.
• ❤️‍🩹 End-to-end optimization of exploration strategies requires trajectories that explore and execute, leading to difficulties in learning exploration separately.
• ❓ Posterior sampling and learning dynamics and rewards provide alternatives to decouple exploration from execution, although they have their limitations.

Transcript

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Questions & Answers

Q: What is the goal of meta RL?

The goal of meta RL is to learn how to explore and solve new tasks quickly by leveraging the knowledge gained from previous tasks.

Q: Why is end-to-end optimization of exploration strategies challenging?

End-to-end optimization requires trajectories that explore and execute the task, making it difficult to learn exploration strategies separately from execution.

Q: How does posterior sampling work in meta RL?

Posterior sampling involves learning how to solve tasks and maintaining a distribution over task representations. The exploration policy samples from this distribution to explore different tasks and improve exploration strategies.

Q: What is the advantage of learning dynamics and rewards in exploration strategies?

Learning dynamics and rewards allows the exploration policy to gain knowledge about the task and predict the dynamics and rewards more accurately, leading to more efficient exploration.

Q: What are the downsides of the explored meta RL approaches?

Posterior sampling may explore sub-optimal strategies, and learning dynamics and rewards may not generalize well to new tasks or environments.

Summary & Key Takeaways

• Meta RL focuses on learning how to explore and solve tasks quickly.

• End-to-end optimization of exploration strategies is challenging due to the coupling problem between exploration and execution.

• Alternative strategies, such as posterior sampling and learning dynamics and rewards, provide more efficient and targeted exploration approaches.