Abstract
Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or correlation of the reward with external factors that are not known to the agent. Corruption or stochasticity of the reward signal can be especially problematic in robotics, where goal specification can be particularly difficult for complex tasks. While many variance reduction techniques have been studied to improve the robustness of the RL process, handling such stochastic or corrupted reward structures remains difficult. As an alternative for handling this scenario in model-free RL methods, we suggest using an estimator for both rewards and value functions. We demonstrate that this improves performance under corrupted stochastic rewards in both the tabular and non-linear function approximation settings for a variety of noise types and environments. The use of reward estimation is a robust and easy-to-implement improvement for handling corrupted reward signals in model-free RL.
Original language | English (US) |
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Pages (from-to) | 674-699 |
Number of pages | 26 |
Journal | Proceedings of Machine Learning Research |
Volume | 87 |
State | Published - 2018 |
Externally published | Yes |
Event | 2nd Conference on Robot Learning, CoRL 2018 - Zurich, Switzerland Duration: Oct 29 2018 → Oct 31 2018 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability
Keywords
- Goal Specification
- Reinforcement Learning
- Uncertainty