Dynamic Programming to Artificial Intelligence: Q-Learning

Dynamic Programming to Artificial Intelligence: Q-Learning

Dynamic Programming to Artificial Intelligence: Q-Learning. A failure is not always a mistake, it may simply be the best one can do under the circumstances. The real mistake is overfitting.

A failure is not always a mistake, it may simply be the best one can do under the circumstances. The real mistake is to stop trying. — B. F. Skinner

Reinforcement learning models are beating human players in games around the world. Huge international companies are investing millions in reinforcement learning. Reinforcement learning in today’s world is so powerful because it requires neither data nor labels. It could be a technique that leads to general artificial intelligence.

Supervised and Unsupervised Learning

As a summary, in supervised learning, a model learns to map input to outputs using predefined and labeled data. An unsupervised learning approach teaches a model to cluster and group *data using *predefined data.

Reinforcement Learning

However, in reinforcement learning, the model receives no data set and guidance, using a trial and error approach.

Reinforcement learning is an area of machine learning defined by how some model (called agent in reinforcement learning) behaves in an environment to maximize a given reward. The most similar real-world example is of a wild animal trying to find food in its ecosystem. In this example, the animal is the agent, the ecosystem is the environment, and the food is the reward.

Reinforcement learning is frequently used in the domain of game playing, where there is no immediate way to label how “good” an action was, since we would need to consider all future outcomes.

Markov Decision Processes

The Markov Decision Process is the most fundamental concept of reinforcement learning. There are a few components in an MDP that interact with each other:

  • Agent — the model
  • Environment — the overall situation
  • State — the situation at a specific time
  • Action — how the agent acts
  • Reward — feedback from the environment

MDP Notation

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An agent receives information about its current state from the environment, makes an action, and receives a reward. The process repeats. Source: Sutton, R. S. and Barto, A. G. Introduction to Reinforcement Learning

To repeat what was previously discussed in more mathematically formal terms, some notation must be defined.

  • t represents the current time step
  • S is the set of all possible states, with St_ being the state at time t
  • A is the set of all possible actions, with At_ being the action performed at time t
  • R is the set of all possible rewards, with Rt_ being the reward received after performing A(t-1)_
  • T is the last time step (the last step happens when a certain condition is reached or t is higher than a value)

The process can be written as:

  1. The agent receives a state St_
  2. The agent performs an action At_ based on St_
  3. The agent receives a reward R(t+1)_
  4. The environments transitions into a new state S(t+1)_
  5. The cycle repeats for t+1

reinforcement-learning artificial-intelligence machine-learning q-learning

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