Benchmark Environments for Multitask Learning in Continuous Domains
  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.
