Revisiting the NetHack Learning Environment

The NetHack Learning Environment (NLE) was proposed as a challenging benchmark to test an agents abilities to perform complex reasoning over long time horizons in a stochastic, partially-observed, procedurally generated setting. To date, no approach, including those based on reinforcement learning, using large pretrained models, using handcoded symbolic agents, imitating expert trajectories or any hybrid method has achieved significant progress towards completing the game. We take a deeper look into the mechanics and interface of the NLE and show that much of the complexity of NetHack is inaccessible due to constraints on the observation and action spaces. We propose a series of modifications and show that they meaningfully improve performance on the NLE.