AI engineering glossary · Reinforcement learning
What is maximum entropy reinforcement learning?
Maximum entropy reinforcement learning: A framework in which the learner tries to earn high scores while keeping its behaviour as random as possible, by adding an entropy term to the objective. It gives better exploration, robustness to disturbances and multiple near-optimal behaviours.
Maximum entropy reinforcement learning: an example
A robot that has two equally good routes around an obstacle learns to use both instead of committing to one.
Related terms
- Soft actor-critic
- Entropy regularisation
- Stochastic policy
- Exploration
More Reinforcement learning terms
- Model predictive control
- Model-based
- Model-free
- Monte Carlo methods (reinforcement learning)
- Monte Carlo tree search
- Multi-agent reinforcement learning
- Multi-armed bandit
- MuZero
Where you learn it
Maximum entropy reinforcement learning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.