SPIBB-DQN: Safe Batch Reinforcement Learning with Function Approximation - Laroche, Romain; Tachet des Combes, Remi (The 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019)
We consider Safe Policy Improvement (SPI) in Batch Reinforcement Learning (Batch RL): from a fixed dataset and without direct access to the true environment, train a policy that is guaranteed to perform at least as well as the baseline policy used to collect the data. Our contribution is a model-free version of the SPI with Baseline Bootstrapping (SPIBB) algorithm, called SPIBB-DQN, which consists in applying the Bellman update only in state-action pairs that have been sufficiently sampled in the batch. In low-visited parts of the environment, the trained policy reproduces the baseline. We show its benefits on a navigation task and on CartPole. SPIBBDQN is, to the best of our knowledge, the first RL algorithm relying on a neural network representation able to train efficiently and reliably from batch data, without any interaction with the environment.