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Demonstration Guided Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting.
Go-Explore for Residential Energy Management
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.
Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems.
A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming
To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial.
Modelling Electricity Consumption in Irish Dairy Farms Using Agent-Based Modelling
In this research paper, an agent-based model is proposed to model the electricity consumption of Irish dairy farms.
Reinforcement Learning for Battery Management in Dairy Farming
Effective battery management is essential for renewable integration within the agriculture sector.
Evolving Multi-Objective Neural Network Controllers for Robot Swarms
The results also confirm that multi-objective neural network controllers evolved in a low-fidelity simulator can be transferred to high-fidelity simulated environments and that the controllers can scale to environments with a larger number of robots without further retraining needed.
Evolutionary Strategy Guided Reinforcement Learning via MultiBuffer Communication
Evolutionary Algorithms and Deep Reinforcement Learning have both successfully solved control problems across a variety of domains.
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach
In such problems, it is not always possible to know the preferences of a decision-maker for different objectives.
A Review of Reinforcement Learning for Autonomous Building Energy Management
The area of building energy management has received a significant amount of interest in recent years.
