The value iteration algorithm is an iterative method used to compute the optimal value function in reinforcement learning. It calculates the value of each state under the optimal policy by repeatedly updating state values until they converge. The update is based on the Bellman equation, which expresses the value of a state as the maximum expected return from all possible actions.
In value iteration, the algorithm starts with arbitrary values for all states and then iteratively updates the value of each state. Each iteration involves calculating the expected rewards for all possible actions and selecting the maximum one. This continues until the value function stabilizes and converges to the optimal values.
Value iteration is guaranteed to find the optimal policy, but it can be computationally expensive for large state spaces, as it requires updating every state value in each iteration.