The NetHack Learning Environment (2006.13760v2)

Abstract: Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation, they are rarely both. Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack. We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL, while dramatically reducing the computational resources required to gather a large amount of experience. We compare NLE and its task suite to existing alternatives, and discuss why it is an ideal medium for testing the robustness and systematic generalization of RL agents. We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration, alongside qualitative analysis of various agents trained in the environment. NLE is open source at https://github.com/facebookresearch/nle.

An Analysis of the NetHack Learning Environment for Reinforcement Learning Research

The paper introduces the NetHack Learning Environment (NLE), a novel reinforcement learning (RL) platform based on the traditional roguelike game, NetHack. The environment is designed to address the gap between complex, stochastic challenges and the computational efficiency needed for RL research. In this essay, we will examine the motivations, architecture, and implications of utilizing NLE as a benchmark for RL algorithms.

Motivation and Background

Advancements in RL are often propelled by challenging environments that scrutinize existing methodologies. However, prior environments either lacked complexity or were computationally demanding, limiting their applicability. The Arcade Learning Environment and more recent environments like StarCraft and Minecraft introduced some unique challenges but were either too deterministic or computationally expensive.

The NetHack Learning Environment offers an attractive balance by leveraging a complex, procedurally generated environment that remains computationally efficient. NetHack, with its dense library of entities and stochastic dynamics, naturally requires exploration, planning, and skill acquisition, making it ideal for RL research.

Features of the NetHack Learning Environment

NLE is structured as a Gym environment built from the terminal-based game NetHack, providing a rich RL testbed. The choice of NetHack, a game developed in 1987, is particularly promising because of its procedural generation and broad state-space involving hundreds of entities and intricate dynamics. Traditional RL exploratory methods like Go-Explore, which rely on determinism, are shown to be less effective in the face of NetHack's immense variability.

Key features of the NLE include:

• Procedurally Generated Complexity: Each game session generates unique dungeon layouts with stochastic elements, fostering robust test conditions for an agent's ability to generalize.
• Wide and Diverse Entity Set: The presence of numerous monsters, items, and environmental features offers in-depth challenges for skill acquisition and long-term planning.
• Symbolic Observation Space: The use of a symbolic representation instead of pixel data aids in efficient state representation and processing.

Experimental Results and Implications

The paper presents several RL tasks (e.g., navigating to staircases, collecting gold, and scoring) to evaluate agent performance. Models trained using IMPALA and Random Network Distillation (RND) showed promising performance in initial exploration tasks, albeit with varying success across different character roles. The stochastic nature of NetHack proved challenging, particularly for exploration-heavy tasks like locating the Oracle.

The experiments underscore the environment's potential to advance research in several domains:

• Exploration Methods: As standard exploration heuristics face limitations, NLE stimulates the development of novel exploration strategies.
• Skill Transfer and Generalization: The procedural setup provides a sandbox for testing systematic generalization and transfer learning in RL.
• Lifelong and Hierarchical Learning: With long episode horizons and complex multi-level dependencies, NLE contributes a conducive environment for lifelong learning research.

Looking Forward

NLE's release marks a significant milestone by providing a sophisticated yet accessible testbed for both computationally constrained laboratories and RL pioneers. Its computational efficiency facilitates broader participation in RL challenges, promoting an inclusive research environment. Future developments could expand on scripting capabilities, enabling tailored sandbox tasks that tap into NetHack's vast universe of interactive phenomena.

In conclusion, the NetHack Learning Environment presents itself as an exemplary long-term RL research platform. By introducing complexities in a fast-simulating context, it invites the community to advance algorithms capable of real-world applications where unpredictability and intricacy prevail.