Published on Thu Dec 05 2019

Inter-Level Cooperation in Hierarchical Reinforcement Learning

Abdul Rahman Kreidieh, Glen Berseth, Brandon Trabucco, Samyak Parajuli, Sergey Levine, Alexandre M. Bayen

Hierarchical models for deep reinforcement learning (RL) have emerged as powerful methods for generating meaningful control strategies in difficult long time horizon tasks. Training of said hierarchical models, however, continue to suffer from instabilities that limit their applicability.

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Abstract

Hierarchical models for deep reinforcement learning (RL) have emerged as powerful methods for generating meaningful control strategies in difficult long time horizon tasks. Training of said hierarchical models, however, continue to suffer from instabilities that limit their applicability. In this paper, we address instabilities that arise from the concurrent optimization of goal-assignment and goal-achievement policies. Drawing connections between this concurrent optimization scheme and communication and cooperation in multi-agent RL, we redefine the standard optimization procedure to explicitly promote cooperation between these disparate tasks. Our method is demonstrated to achieve superior results to existing techniques in a set of difficult long time horizon tasks, and serves to expand the scope of solvable tasks by hierarchical reinforcement learning. Videos of the results are available at: https://sites.google.com/berkeley.edu/cooperative-hrl.