A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data
In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a Conservative Q-Learning agent using d3rlpy. By structuring the workflow around offline…
