How to Tame Your Deep RL

Running reinforcement learning (RL) experiments—especially when implementing models yourself—is notoriously more challenging than typical supervised learning (SL) experiments (as attested by none other than Andrej Karpathy here ). SL models are generally well-behaved: they converge predictably, respond well to cross-validation, and scale efficiently on GPUs. Deep RL models, however, are entirely…