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McGill Mobile Robotics Lab
Benchmark Environments for Multitask Learning in Continuous Domain
Call for contributions! We're always looking for more multitask learning benchmarks, environments, and better documentation. Please send us a send us a pull request!

Example environments: 2D navigation task, with several sample paths (top-left). Hopper with a wall (top-right). Walker2d with big (bottom-right) and small (bottom-left) feet.
As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit. In discrete domains, performance on the Atari game suite has emerged as the de facto benchmark for assessing multitask learning. However, in continuous domains there is a lack of agreement on standard multitask evaluation environments which makes it difficult to compare different approaches fairly. In this work, we describe a benchmark set of tasks that we have developed in an extendable framework based on OpenAI Gym. We run a simple baseline using Trust Region Policy Optimization and release the framework publicly to be expanded and used for the systematic comparison of multitask, transfer, and lifelong learning in continuous domains.