Using Unity to Help Solve Intelligence
arXiv:2011.09294
Abstract
In the pursuit of artificial general intelligence, our most significant measurement of progress is an agent's ability to achieve goals in a wide range of environments. Existing platforms for constructing such environments are typically constrained by the technologies they are founded on, and are therefore only able to provide a subset of scenarios necessary to evaluate progress. To overcome these shortcomings, we present our use of Unity, a widely recognized and comprehensive game engine, to create more diverse, complex, virtual simulations. We describe the concepts and components developed to simplify the authoring of these environments, intended for use predominantly in the field of reinforcement learning. We also introduce a practical approach to packaging and re-distributing environments in a way that attempts to improve the robustness and reproducibility of experiment results. To illustrate the versatility of our use of Unity compared to other solutions, we highlight environments already created using our approach from published papers. We hope that others can draw inspiration from how we adapted Unity to our needs, and anticipate increasingly varied and complex environments to emerge from our approach as familiarity grows.
References in corpus (12)
- An introduction to Docker for reproducible research, with examples from the R environment
- Dota 2 with Large Scale Deep Reinforcement Learning
- Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
- StarCraft II: A New Challenge for Reinforcement Learning
- IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
- Unity: A General Platform for Intelligent Agents
- DeepMind Control Suite
- Massively Parallel Methods for Deep Reinforcement Learning
- Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
- Gotta Learn Fast: A New Benchmark for Generalization in RL
- Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents
- Generalization of Reinforcement Learners with Working and Episodic Memory