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In this video, the concept of Markov decision processes (MDPs) is introduced as a formal way to model sequential decision-making in reinforcement learning. MDPs serve as the bedrock for reinforcement learning, where an agent interacts with the environment and takes actions based on its state and rewards. MDPs are represented with mathematical notation, where the probability distributions of time and state are dependent on preceding states and actions. Viewers are encouraged to familiarize themselves with the mathematical notation and utilize the blog and Deep Lizard Hivemind for more information.
In this section, we learn about Markov decision processes (MDPs), which formalize sequential decision-making and are the basis for reinforcement learning. A decision maker, or agent, interacts with the environment and takes actions based on the state it's in and the rewards it wants to maximize. This process of selecting an action, transitioning to a new state, and receiving a reward happens over and over again, creating trajectories. MDPs are represented with mathematical notation, where each state, action, and reward has a finite number of elements. Random variables for time and state have well-defined probability distributions, dependent on preceding states and actions.
In this section, the speaker introduces the concept of Markov decision processes (MDPs) as a formal way to model sequential decision-making in reinforcement learning. The agent and environment interact with each other over time, with MDPs serving as the bedrock for reinforcement learning. The speaker encourages viewers to familiarize themselves with the mathematical notation and utilize the blog and Deep Lizard Hivemind for more information.
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