Stanford CS234 Reinforcement Learning I Exploration 3 I 2024 I Lecture 13

This lecture delves into effective exploration strategies in reinforcement learning, focusing on both tabular and generalized environments. It starts by examining optimism in the face of uncertainty and Thompson sampling within multi-armed bandit scenarios. The discussion then progresses to Markov Decision Processes (MDPs), employing algorithms like MBIB and PSRL. The lecture also addresses the challenges of large state spaces and explores solutions such as linear reward models and pseudo-counts. Additionally, it investigates advanced techniques like DREAM and decision pre-trained transformers aimed at improving meta-reinforcement learning across various tasks. A crucial insight from the lecture is that successful exploration requires a delicate balance between understanding the environment (exploration) and making informed decisions based on existing knowledge (exploitation). Leveraging the structural aspects of a problem significantly enhances overall performance.

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Stanford Online

Multi-Armed Bandits Refresher and Problem Solving

Exploration in Reinforcement Learning: The Breakout Game Example

Efficient Reinforcement Learning in Tabular Markov Decision Processes

Model-Based Interval Estimation and the Simulation Lemma

Bayesian Markov Decision Processes and the PSRL Algorithm

Strategic Exploration and the Challenges of Frequent Updates

Concurrent Reinforcement Learning and Seed Sampling

Generalization in Reinforcement Learning: Contextual Bandits and Beyond

Meta-Exploration and Decision Pre-trained Transformers

Stanford CS234 Reinforcement Learning I Exploration 3 I 2024 I Lecture 13

Stanford Online

00:05Multi-Armed Bandits Refresher and Problem Solving

Multi-Armed Bandits Refresher and Problem Solving

03:00Exploration in Reinforcement Learning: The Breakout Game Example

Exploration in Reinforcement Learning: The Breakout Game Example

09:45Efficient Reinforcement Learning in Tabular Markov Decision Processes

Efficient Reinforcement Learning in Tabular Markov Decision Processes

17:46Model-Based Interval Estimation and the Simulation Lemma

Model-Based Interval Estimation and the Simulation Lemma

31:57Bayesian Markov Decision Processes and the PSRL Algorithm

Bayesian Markov Decision Processes and the PSRL Algorithm

43:35Strategic Exploration and the Challenges of Frequent Updates

Strategic Exploration and the Challenges of Frequent Updates

52:19Concurrent Reinforcement Learning and Seed Sampling

Concurrent Reinforcement Learning and Seed Sampling

59:58Generalization in Reinforcement Learning: Contextual Bandits and Beyond

Generalization in Reinforcement Learning: Contextual Bandits and Beyond

1:07:15Meta-Exploration and Decision Pre-trained Transformers

Meta-Exploration and Decision Pre-trained Transformers