Model-Based Deep RL: Planning and VAST

This lecture introduces model-based reinforcement learning, focusing on planning in the background. It covers the estimation of transition dynamics and reward structure, the use of models for planning, variational state tabulation, decision time planning, and AlphaZero and MuZero algorithms. The instructor emphasizes the efficiency of updating Q- and V-values through value iteration in model-based reinforcement learning.