Reinforcement Learning and Deep RL Python Theory and Projects - MDP (Markov Decision Process)

The video tutorial explains the Markov Decision Process (MDP) as a fundamental concept in deep learning and reinforcement learning. It describes how an agent interacts with the environment through actions, receiving rewards and transitioning between states. The tutorial emphasizes the importance of choosing actions based on rewards and states, illustrating this with examples like investing in cryptocurrency. It highlights the role of timing and state dependency in decision making, concluding that MDP is a key component of reinforcement learning algorithms.

What are the consequences of receiving positive or negative rewards in MDP?

Explain how the state of the environment influences the actions taken by the agent.