Title: Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning. Abstract: Representation learning on visualized input is an important yet challenging task for deep reinforcement learning (RL). The feature space learned from visualized input not only dominates the agent's generalization ability in new environments but also affect the data efficiency during training. To help the RL agent learn general and discriminative representation among various states, we present cross-state self-constraint(CSSC), a novel constraint that regularizes the representation feature space by comparing similarity of different pairs of representations. Based on the representation-behavior connection derived from the agent's experience, this constraint helps reinforce the general feature recognition during the learning process and thus enhance the generalization to unseen environment. We test our proposed method on the OpenAI ProcGen benchmark and see significant improvement on generalization performance across most of ProcGen games.