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This video covers the first visit Monte Carlo algorithm, a model-free reinforcement learning algorithm. The algorithm makes a few assumptions about the environment and uses a full episode to update the policy. This video covers how the algorithm works and how it can be used to calculate the value function of a policy.

**00:00:00**In this video, we introduce a model-free algorithm for reinforcement learning, called monte carlo. The algorithm makes a few assumptions about the environment, and then uses a full episode to update the policy.**00:05:00**The video introduces a first RL algorithm, first visit Monte Carlo, which calculates the value function of a policy given an environment and a set of cards. This algorithm can be thought of as a sampling procedure, estimating the expected future sum of discounted rewards.**00:10:00**This video introduces a first-order RL algorithm for a grid world, demonstrating how it calculates the value of a state according to its rewards and punishments.**00:15:00**This video introduces the concept of algorithms for reinforcement learning, including model-based and model-free methods. It also covers the first visit Monte Carlo algorithm.

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