Q-learning with online random forests. (arXiv:2204.03771v1 [stat.ML])

$Q$-learning is the most fundamental model-free reinforcement learning
algorithm. Deployment of $Q$-learning requires approximation of the
state-action value function (also known as the $Q$-function). In this work, we
provide online random forests as $Q$-function approximators and propose a novel
method wherein the random forest is grown as learning proceeds (through
expanding forests). We demonstrate improved performance of our methods over
state-of-the-art Deep $Q$-Networks in two OpenAI gyms (`blackjack' and
`inverted pendulum') but not in the `lunar lander' gym. We suspect …

arxiv learning ml q-learning random random forests