Introduction to Reinforcement Learning

I. Introduction to Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions based on its past experiences (exploitation) and also by new choices (exploration).

II. Bandit Algorithms

Bandit algorithms are a class of algorithms used in reinforcement learning to balance the exploration and exploitation trade-off. They are called 'bandit algorithms' because they are based on the 'multi-armed bandit problem' in probability theory.

A. Upper Confidence Bound (UCB) algorithm

The UCB algorithm is a bandit algorithm that balances exploration and exploitation by choosing the action that has the maximum upper confidence bound.

B. Probably Approximately Correct (PAC) algorithm

The PAC algorithm is another bandit algorithm that aims to minimize the total regret, or the loss incurred due to not choosing the optimal action.

C. Median Elimination algorithm

The Median Elimination algorithm is a bandit algorithm that eliminates the arms with the lowest estimated rewards, thus narrowing down the choices to the potentially best arms.

III. Policy Gradient

Policy gradient methods are a type of reinforcement learning techniques that optimize the policy directly. The policy is a mapping from states to actions.

A. Policy gradient theorem

The policy gradient theorem provides the gradient of the expected return by averaging over the state-action distribution under the current policy.

B. Reinforce algorithm

The REINFORCE algorithm is a policy gradient method that uses Monte Carlo sampling to estimate the expected return.

IV. Full RL & MDPs

Full reinforcement learning involves learning a policy that can map every state to an action that maximizes the expected return from that state. Markov Decision Processes (MDPs) provide a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.

A. Value iteration algorithm

The value iteration algorithm is a method used in reinforcement learning to find the optimal policy in a Markov Decision Process.

B. Q-learning algorithm

Q-learning is a values based algorithm in reinforcement learning. It is used to find the optimal action-selection policy using a q function.

V. Bellman Optimality

Bellman Optimality Equation is a fundamental equation in reinforcement learning that expresses the value of a state under an optimal policy in terms of the expected immediate reward and the expected value of the successor state.

VI. Advantages and Disadvantages of Reinforcement Learning

Reinforcement learning has several advantages such as ability to learn from interaction, ability to handle problems with stochastic transitions and rewards, without requiring adaptations. However, it also has several disadvantages such as high variance, often requiring large amounts of data to converge to an optimal policy.

VII. Conclusion

Reinforcement learning is a powerful tool for teaching machines to perform tasks autonomously. Its ability to learn from interaction and its flexibility make it a powerful tool for a wide range of tasks, from playing games to driving cars.

Summary

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. Bandit algorithms, policy gradient methods, and Markov Decision Processes (MDPs) are some of the key concepts in RL. The Bellman Optimality Equation is a fundamental equation in RL. RL has several advantages such as ability to learn from interaction, but it also has several disadvantages such as high variance.

Analogy

Imagine you're playing a game of chess. At each move, you have a number of different options, or 'actions', you can take. Some of these actions will lead to you winning the game, while others will lead to you losing. You don't know the outcome of each action until you take it, but you can learn from your past actions to make better decisions in the future. This is the basic idea behind reinforcement learning.