Abstract

The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 presents baseline results of an agent trained by deep imitation or reinforcement learning. BabyAI 1.1 improves the agent’s architecture in three minor ways. This increases reinforcement learning sample efficiency by up to $3\times$ and improves imitation learning performance on the hardest level from $77\%$ to $90.4\%$ . We hope that these improvements increase the computational efficiency of BabyAI experiments and help users design better agents.