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Evolving hierarchical memory-prediction machines in multi-task reinforcement learning

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Abstract

A fundamental aspect of intelligent agent behaviour is the ability to encode salient features of experience in memory and use these memories, in combination with current sensory information, to predict the best action for each situation such that long-term objectives are maximized. The world is highly dynamic, and behavioural agents must generalize across a variety of environments and objectives over time. This scenario can be modeled as a partially-observable multi-task reinforcement learning problem. We use genetic programming to evolve highly-generalized agents capable of operating in six unique environments from the control literature, including OpenAI’s entire Classic Control suite. This requires the agent to support discrete and continuous actions simultaneously. No task-identification sensor inputs are provided, thus agents must identify tasks from the dynamics of state variables alone and define control policies for each task. We show that emergent hierarchical structure in the evolving programs leads to multi-task agents that succeed by performing a temporal decomposition and encoding of the problem environments in memory. The resulting agents are competitive with task-specific agents in all six environments. Furthermore, the hierarchical structure of programs allows for dynamic run-time complexity, which results in relatively efficient operation.

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Notes

  1. With the parameters listed in Table 4, the team generation process creates 1575 new agents in each generation.

  2. The population at any given generation includes 1575 new agents and 1575 elite agents from previous generations. The initial population size (\(R_{size}\) in Table 4) is 1000. Thus, after 2 generations the 63 bins of elites will remain full, their content being recalculated in each generation based on the fitness of new agents.

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Acknowledgements

S.K. gratefully acknowledges support through the NSERC Postdoctoral Scholarship program. This material is based in part upon work supported by the National Science Foundation under Cooperative Agreement No. DBI-0939454 to the BEACON Center for Evolution in Action at Michigan State University. W.B. acknowledges support from the John R. Koza Endowment fund for part of this work. Michigan State University provided computational resources through the Institute for Cyber-Enabled Research. Additional support provided by ACENET, Calcul Québec, Compute Ontario and WestGrid, and Compute Canada (www.computecanada.ca). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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  1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA

    Stephen Kelly, Tatiana Voegerl, Wolfgang Banzhaf & Cedric Gondro

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Kelly, S., Voegerl, T., Banzhaf, W. et al. Evolving hierarchical memory-prediction machines in multi-task reinforcement learning. Genet Program Evolvable Mach 22, 573–605 (2021). https://doi.org/10.1007/s10710-021-09418-4

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