An Ensemble of Linearly Combined Reinforcement-Learning Agents

Reinforcement-learning (RL) algorithms are often tweaked and tunedto specific environments when applied, calling into question whetherlearning can truly be considered autonomous in these cases. In thiswork, we show how more robust learning across environments is possibleby adopting an ensemble approach to reinforcement learning. Our approachlearns a weighted linear combination of Q-values from multiple independentlearning algorithms. In our evaluations in generalized RL environments,we find that the algorithm compares favorably to the best tuned algorithm.Our work provides a promising basis for further study into the useof ensemble methods in RL.