Episode 43: Deep Reinforcement Learning
The Theory of Anything
00:00
Stochastic Environments and Markov Decision Process
The chapter discusses the concept of stochastic environments and how an agent can learn an optimal path using a Markov Decision Process (MDP). It provides real-life examples to illustrate the concept and mentions the challenges of using a Q table with a large number of entries.
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