Reinforcement learning
Knowing this, we can talk about the main loop for a reinforcement learning episode. The agent interacts with the environment in discrete time steps. Think of it like the "tick-tock" of a clock. With discrete time, things only happen during the "ticks" and the "tocks", and not in between. At each time , the agent observes the environment's state  and picks an action  based on a policy . The next time step, the agent receives a reward signal  and a new observation . The value function  is updated using the reward. This continues until a terminal state  is reached.