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This paper introduces a set of formally defined and transparent problems forreinforcement learning algorithms with the following characteristics: (1)variable degrees of observability (non-Markov observations), (2) distal andsparse rewards, (3) variable and hierarchical reward structure, (4)multiple-task generation, (5) variable problem complexity. The environmentprovides 1D or 2D categorical observations, and takes actions as input. Thecore structure of the CT-graph is a multi-branch tree graph with arbitrarybranching factor, depth, and observation sets that can be varied to increasethe dimensions of the problem in a controllable and measurable way. Two maincategories of states, decision states and wait states, are devised to create ahierarchy of importance among observations, typical of real-world problems. Alarge observation set can produce a vast set of histories that impairsmemory-augmented agents. Variable reward functions allow for the easy creationof multiple tasks and the ability of an agent to efficiently adapt in dynamicscenarios where tasks with controllable degrees of similarities are presented.Challenging complexity levels can be easily achieved due to the exponentialgrowth of the graph. The problem formulation and accompanying code provide afast, transparent, and mathematically defined set of configurable tests tocompare the performance of reinforcement learning algorithms, in particular inlifelong learning settings.

The configurable tree graph (CT-graph): measurable problems in partially  observable and distal reward environments for lifelong reinforcement learning