#
Glossary

This is a community-created glossary. Contributions are welcome!

- Deep Q-Learning: A value-based deep reinforcement learning algorithm that uses a deep neural network to approximate Q-values for actions in a given state. The goal of Deep Q-learning is to find the optimal policy that maximizes the expected cumulative reward by learning the action-values.

- Value-based methods: Reinforcement Learning methods that estimate a value function as an intermediate step towards finding an optimal policy.

- Policy-based methods: Reinforcement Learning methods that directly learn to approximate the optimal policy without learning a value function. In practice they output a probability distribution over actions.

  The benefits of using policy-gradient methods over value-based methods include:

  - simplicity of integration: no need to store action values;
  - ability to learn a stochastic policy: the agent explores the state space without always taking the same trajectory, and avoids the problem of perceptual aliasing;
  - effectiveness in high-dimensional and continuous action spaces; and
  - improved convergence properties.

- Policy Gradient: A subset of policy-based methods where the objective is to maximize the performance of a parameterized policy using gradient ascent. The goal of a policy-gradient is to control the probability distribution of actions by tuning the policy such that good actions (that maximize the return) are sampled more frequently in the future.

- Monte Carlo Reinforce: A policy-gradient algorithm that uses an estimated return from an entire episode to update the policy parameter.

If you want to improve the course, you can open a Pull Request.

This glossary was made possible thanks to:

- Diego Carpintero

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