Coevolutionary Deep Reinforcement Learning
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2021, 00, pp. 2600-2607
- Issue Date:
- 2021-01-05
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Coevolutionary_Deep_Reinforcement_Learning.pdf | Published version | 218.91 kB |
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The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for this, selfplay, allows reinforcement learning to develop high performing policies without large datasets or expert knowledge. Despite these benefits, self-play is known to be less sample efficient and suffer unstable learning dynamics. This is in part due to a nonstationary learning problem where an agent's actions influence their opponents and as a consequence the training data they receive. In this paper we demonstrate that competitive pressures can be utilised to improve self-play. This paper leverages coevolution, an evolutionary inspired process in which individuals are compelled to innovate and adapt, to optimise the training of a population of reinforcement learning agents. We demonstrate that our algorithm improves the final performance of a Rainbow DQN trained in the game Connect Four, achieving a 15% higher win percentage over the next leading self-play algorithm. Furthermore, our algorithm exhibits more stable training with less variation in evaluation performance.
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