Go-Explore for Residential Energy Management
This work addresses energy cost optimization for residential users, but it is incremental as it applies an existing algorithm to a specific domain.
The paper tackled the problem of deceptive and sparse rewards in residential energy management by applying the Go-Explore algorithm, achieving up to 19.84% improvement in cost savings compared to existing reinforcement learning methods.
Reinforcement learning is commonly applied in residential energy management, particularly for optimizing energy costs. However, RL agents often face challenges when dealing with deceptive and sparse rewards in the energy control domain, especially with stochastic rewards. In such situations, thorough exploration becomes crucial for learning an optimal policy. Unfortunately, the exploration mechanism can be misled by deceptive reward signals, making thorough exploration difficult. Go-Explore is a family of algorithms which combines planning methods and reinforcement learning methods to achieve efficient exploration. We use the Go-Explore algorithm to solve the cost-saving task in residential energy management problems and achieve an improvement of up to 19.84\% compared to the well-known reinforcement learning algorithms.