The NetHack Learning Environment is a novel research environment for testing the robustness and systematic generalization of reinforcement learning (RL) agents. The environment is based on NetHack , one of the oldest and most popular procedurally generated roguelike games. Existing RL environments are either sufficiently complex or based on fast simulation, but they are rarely both. In contrast, the NetHack Learning Environment combines lightning-fast simulation with very complex game dynamics that are difficult even for humans to master. This allows our agents to experience billions of steps in the environment in a reasonable time frame while still challenging the limits of what current methods can achieve, driving long-term research on topics such as exploration, planning, skill acquisition, and language-conditioned RL.