Steering approaches to Pareto-optimal multiobjective reinforcement learning
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Neurocomputing, 2017, 263, pp. 26-38
- Issue Date:
- 2017-11-08
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0925231217311013-main.pdf | 2.51 MB |
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For reinforcement learning tasks with multiple objectives, it may be advantageous to learn stochastic or non-stationary policies. This paper investigates two novel algorithms for learning non-stationary policies which produce Pareto-optimal behaviour (w-steering and Q-steering), by extending prior work based on the concept of geometric steering. Empirical results demonstrate that both new algorithms offer substantial performance improvements over stationary deterministic policies, while Q-steering significantly outperforms w-steering when the agent has no information about recurrent states within the environment. It is further demonstrated that Q-steering can be used interactively by providing a human decision-maker with a visualisation of the Pareto front and allowing them to adjust the agent's target point during learning. To demonstrate broader applicability, the use of Q-steering in combination with function approximation is also illustrated on a task involving control of local battery storage for a residential solar power system.
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