Reinforcement Learning and Deep RL Python Theory and Projects - State

The video tutorial explains the concepts of state and action in reinforcement learning, highlighting how states are snapshots of the environment at a given time. It discusses how state changes occur when an agent moves or when the environment changes, affecting decision-making processes. The tutorial emphasizes that state is primarily dependent on the environment, not just the agent's actions, and illustrates this with examples of state changes due to external events.

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