Reinforcement Learning Behaviors

We’ve published a library of behaviors and example simulations demonstrating how to implement the popular Q-learning reinforcement algorithm in a HASH simulation. The library contains a generic set of Q-learning behaviors that can be added to an agent to train it to take an optimal action in its environment. The simulations are:

In both simulations you can see how the agent’s rewards converge to a steady state where it has, over many iterations, learned a policy to execute.