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Evaluation of deep reinforcement learning (RL) is inherently challenging. Inparticular, learned policies are largely opaque, and hypotheses about thebehavior of deep RL agents are difficult to test in black-box environments.Considerable effort has gone into addressing opacity, but almost no effort hasbeen devoted to producing high quality environments for experimental evaluationof agent behavior. We present TOYBOX, a new high-performance, open-source*subset of Atari environments re-designed for the experimental evaluation ofdeep RL. We show that TOYBOX enables a wide range of experiments and analysesthat are impossible in other environments.  *https://kdl-umass.github.io/Toybox/

Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning