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.

In reinforcement learning, what is typically used to denote an action?

• The letter E

• The letter A

• The letter R

• The letter S

What happens to the state when an agent moves from one position to another in the environment?

• The action becomes invalid

• The environment resets

• The state changes

• The state remains the same

Why might an action become available to an agent after a state change?

• The action was previously invalid

• The environment has expanded

• The agent has reached a boundary

• The agent's position allows the action

Can the state of the environment change without the agent moving?

• No, the agent must move

• Yes, due to other agents or events

• Only if the agent decides to change it

• Only if the environment is reset

What should be done when the environment changes due to an external event?

• Wait for the agent to move

• Ignore the change

• Make decisions based on the new state

• Revert to the previous state