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

TL;DR

This paper presents the CT-graph, a flexible, mathematically defined environment for testing reinforcement learning algorithms under complex, partially observable, and hierarchical reward scenarios, facilitating systematic comparisons in lifelong learning.

Contribution

It introduces a configurable, transparent environment with hierarchical structure and variable complexity for benchmarking reinforcement learning algorithms in lifelong learning contexts.

Findings

Environment supports variable observability and reward structures.

Enables controlled complexity growth for testing scalability.

Facilitates comparison of RL algorithms in dynamic, multi-task settings.

Abstract

This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observability (non-Markov observations), (2) distal and sparse rewards, (3) variable and hierarchical reward structure, (4) multiple-task generation, (5) variable problem complexity. The environment provides 1D or 2D categorical observations, and takes actions as input. The core structure of the CT-graph is a multi-branch tree graph with arbitrary branching factor, depth, and observation sets that can be varied to increase the dimensions of the problem in a controllable and measurable way. Two main categories of states, decision states and wait states, are devised to create a hierarchy of importance among observations, typical of real-world problems. A large observation set can produce a vast set of histories that impairs memory-augmented agents. Variable reward functions allow for the easy creation of multiple tasks and the ability of an agent to efficiently adapt in dynamic scenarios where tasks with controllable degrees of similarities are presented. Challenging complexity levels can be easily achieved due to the exponential growth of the graph. The problem formulation and accompanying code provide a fast, transparent, and mathematically defined set of configurable tests to compare the performance of reinforcement learning algorithms, in particular in lifelong learning settings.