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 formula_6, the agent observes the environment's state formula_7 and picks an action formula_8 based on a policy formula_1. The next time step, the agent receives a reward signal formula_10 and a new observation formula_11. The value function formula_12 is updated using the reward. This continues until a terminal state formula_13 is reached.