Abstract: We present $\ell$Gym, a new benchmark for language-conditioned reinforcement learning in visual environments. $\ell$Gym is based on 2,661 human-written natural language statements grounded in an interactive visual environment, and emphasizing compositionality and semantic diversity. We annotate all statements with Python programs representing their meaning. The programs are executable in an interactive visual environment to enable exact reward computation in every possible world state. Each statement is paired with multiple start states and reward functions to form thousands of distinct Contextual Markov Decision Processes of varying difficulty. We experiment with $\ell$Gym with different models and learning regimes. Our results and analysis show that while existing methods are able to achieve non-trivial performance, $\ell$Gym forms a challenging open problem.