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.

Discuss the implications of an agent not moving while the environment changes.

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What happens to the state of the environment when an event occurs?

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