Title: Proximal Curriculum for Reinforcement Learning Agents. Abstract: We consider the problem of curriculum design for reinforcement learning (RL) agents in contextual multi-task settings. Existing techniques on automatic curriculum design typically have limited theoretical underpinnings or require domain-specific hyperparameter tuning. To tackle these limitations, we design our curriculum strategy, ProCuRL, from basic principles inspired by the pedagogical concept of Zone of Proximal Development (ZPD). We mathematically derive ProCuRL by formalizing the ZPD concept, which suggests that learning progress is maximized when picking tasks that are neither too hard nor too easy for the learner. We also present a practical variant of ProCuRL that can be directly integrated with deep RL frameworks with minimal hyperparameter tuning. Experimental results on a variety of domains demonstrate the effectiveness of our curriculum strategy over state-of-the-art baselines in accelerating the training process of deep RL agents.