What is Reinforcement Learning?
Reinforcement Learning (RL) is a learning paradigm where an agent interacts with an environment to achieve a goal. A common analogy is navigating a maze: the agent takes actions, changes its state, and receives feedback in the form of rewards.
Imagine training a dog. When the dog obeys a command, it receives a treat (+1 reward); when it doesn’t, it gets nothing (-1 reward). Similarly, in RL, we can assign positive or negative values to actions based on their outcomes.
For instance, consider a robotic dog. Instead of hardcoding specific movement patterns, we allow it to explore various actions. The goal is to navigate from its current position to a designated "room." As it experiments, if it makes progress (+1), it’s rewarded; if it falls or makes a poor choice (-1), it learns from that too. Through repeated trials, the agent develops a strategy to optimize its path.
Additional Reading
- Simple Reinforcement Learning with TensorFlow (opens in a new tab) by Arthur Juliani (2016)