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Effective residential appliance scheduling is crucial for sustainable living. While multi-objective reinforcement learning (MORL) has proven effective in balancing user preferences in appliance scheduling, traditional MORL struggles with limited data in non-stationary residential settings characterized by renewable generation variations. Significant context shifts that can invalidate previously learned policies. To address these challenges, we extend state-of-the-art MORL algorithms with the meta-learning paradigm, enabling rapid, few-shot adaptation to shifting contexts. Additionally, we employ an auto-encoder (AE)-based unsupervised method to detect environment context changes. We have also developed a residential energy environment to evaluate our method using real-world data from London residential settings. This study not only assesses the application of MORL in residential appliance scheduling but also underscores the effectiveness of meta-learning in energy management. Our top-performing method significantly surpasses the best baseline, while the trained model saves 3.28% on electricity bills, a 2.74% increase in user comfort, and a 5.9% improvement in expected utility. Additionally, it reduces the sparsity of solutions by 62.44%. Remarkably, these gains were accomplished using 96.71% less training data and 61.1% fewer training steps.

Dairy farms consume a significant amount of electricity for their operations, and this research focuses on enhancing energy efficiency and minimizing the impact on the environment in the sector by maximizing the utilization of renewable energy sources. This research investigates the application of Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm (DRL), to enhance dairy farming battery management. We evaluate the algorithm's effectiveness based on its ability to reduce reliance on the electricity grid, highlighting the potential of DRL to enhance energy management in dairy farming. Using real-world data our results demonstrate how the PPO approach outperforms Q-learning by 1.62% for reducing electricity import from the grid. This significant improvement highlights the potential of the Deep Reinforcement Learning algorithm for improving energy efficiency and sustainability in dairy farms., Comment: 12 pages, 5 figures, Practical Applications of Agents and Multi-Agent Systems(PAAMS 2024)

In this paper, we explore the optimization of hyperparameters for the Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms using the Tree-structured Parzen Estimator (TPE) in the context of robotic arm control with seven Degrees of Freedom (DOF). Our results demonstrate a significant enhancement in algorithm performance, TPE improves the success rate of SAC by 10.48 percentage points and PPO by 34.28 percentage points, where models trained for 50K episodes. Furthermore, TPE enables PPO to converge to a reward within 95% of the maximum reward 76% faster than without TPE, which translates to about 40K fewer episodes of training required for optimal performance. Also, this improvement for SAC is 80% faster than without TPE. This study underscores the impact of advanced hyperparameter optimization on the efficiency and success of deep reinforcement learning algorithms in complex robotic tasks.

Farm businesses are increasingly adopting renewables to enhance energy efficiency and reduce reliance on fossil fuels and the grid. This shift aims to decrease dairy farms' dependence on traditional electricity grids by enabling the sale of surplus renewable energy in Peer-to-Peer markets. However, the dynamic nature of farm communities poses challenges, requiring specialized algorithms for P2P energy trading. To address this, the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES) has been developed, providing a platform to experiment with Reinforcement Learning techniques. The simulations demonstrate significant cost savings, including a 43% reduction in electricity expenses, a 42% decrease in peak demand, and a 1.91% increase in energy sales compared to baseline scenarios lacking peer-to-peer energy trading or renewable energy sources., Comment: Proc. of the Main Track of 22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, 26th-28th June, 2024, https://www.paams.net/. Includes 6 figures, 1 table and 32 references

Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. Catering to diverse user preferences, traditional reinforcement learning faces amplified challenges in MORL. To address the difficulty of training policies from scratch in MORL, we introduce demonstration-guided multi-objective reinforcement learning (DG-MORL). This novel approach utilizes prior demonstrations, aligns them with user preferences via corner weight support, and incorporates a self-evolving mechanism to refine suboptimal demonstrations. Our empirical studies demonstrate DG-MORL's superiority over existing MORL algorithms, establishing its robustness and efficacy, particularly under challenging conditions. We also provide an upper bound of the algorithm's sample complexity.

Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could help address this challenge. Effective battery management is important for integrating renewable energy generation. Managing battery charging and discharging poses significant challenges because of fluctuations in electrical consumption, the intermittent nature of renewable energy generation, and fluctuations in energy prices. Artificial Intelligence (AI) has the potential to significantly improve the use of renewable energy in dairy farming, however, there is limited research conducted in this particular domain. This research considers Ireland as a case study as it works towards attaining its 2030 energy strategy centered on the utilization of renewable sources. This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting. This research also explores the effect of the proposed algorithm by adding wind generation data and considering additional case studies. The proposed algorithm reduces the cost of imported electricity from the grid by 13.41%, peak demand by 2%, and 24.49% when utilizing wind generation. These results underline how reinforcement learning is highly effective in managing batteries in the dairy farming sector.

The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuses on modelling the adoption rate of photovoltaic (PV) energy within the dairy sector in Ireland. An agent-based modelling approach is introduced to estimate the adoption rate. The model considers grid energy prices, revenue, costs, and maintenance expenses to calculate the probability of PV adoption. The ABM outputs estimate that by year 2022, 2.45% of dairy farmers have installed PV. This is a 0.45% difference to the actual PV adoption rate in year 2022. This validates the proposed ABM. The paper demonstrates the increasing interest in PV systems as evidenced by the rate of adoption, shedding light on the potential advantages of PV energy adoption in agriculture. This study possesses the potential to forecast future rates of PV energy adoption among dairy farmers. It establishes a groundwork for further research on predicting and understanding the factors influencing the adoption of renewable energy.

To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial. This is especially important for energy-intensive farming, like dairy farming. However, integrating renewables and peer-to-peer trading presents challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer markets. Our strategy reduces electricity costs and peak demand by approximately 30% and 24% respectively, while increasing energy sales by 37% compared to the baseline scenario without P2P trading. This demonstrates the effectiveness of our approach., Comment: Proc. of the Artificial Intelligence for Sustainability, ECAI 2023, Eunika et al. (eds.), Sep 30- Oct 1, 2023, https://sites.google.com/view/ai4s. 2023

Dairy farming can be an energy intensive form of farming. Understanding the factors affecting electricity consumption on dairy farms is crucial for farm owners and energy providers. In order to accurately estimate electricity demands in dairy farms, it is necessary to develop a model. In this research paper, an agent-based model is proposed to model the electricity consumption of Irish dairy farms. The model takes into account various factors that affect the energy consumption of dairy farms, including herd size, number of milking machines, and time of year. The outputs are validated using existing state-of-the-art dairy farm modelling frameworks. The proposed agent-based model is fully explainable, which is an advantage over other Artificial Intelligence techniques, e.g. deep learning., Comment: This paper has been accepted at the 2023 Artificial Intelligence for Sustainability (AI4S) Workshop, at 26th European Conference on Artificial Intelligence ECAI 2023

Dairy farming is a particularly energy-intensive part of the agriculture sector. Effective battery management is essential for renewable integration within the agriculture sector. However, controlling battery charging/discharging is a difficult task due to electricity demand variability, stochasticity of renewable generation, and energy price fluctuations. Despite the potential benefits of applying Artificial Intelligence (AI) to renewable energy in the context of dairy farming, there has been limited research in this area. This research is a priority for Ireland as it strives to meet its governmental goals in energy and sustainability. This research paper utilizes Q-learning to learn an effective policy for charging and discharging a battery within a dairy farm setting. The results demonstrate that the developed policy significantly reduces electricity costs compared to the established baseline algorithm. These findings highlight the effectiveness of reinforcement learning for battery management within the dairy farming sector., Comment: This paper has been accepted at the 2023 Artificial Intelligence for Sustainability (AI4S) Workshop, at 26th European Conference on Artificial Intelligence ECAI 2023

Many swarm robotics tasks consist of multiple conflicting objectives. This research proposes a multi-objective evolutionary neural network approach to developing controllers for swarms of robots. The swarm robot controllers are trained in a low-fidelity Python simulator and then tested in a high-fidelity simulated environment using Webots. Simulations are then conducted to test the scalability of the evolved multi-objective robot controllers to environments with a larger number of robots. The results presented demonstrate that the proposed approach can effectively control each of the robots. The robot swarm exhibits different behaviours as the weighting for each objective is adjusted. 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., Comment: This paper was presented at the 2023 Autonomous Robots and Multirobot Systems (ARMS) Workshop, at The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)

Evolutionary Algorithms and Deep Reinforcement Learning have both successfully solved control problems across a variety of domains. Recently, algorithms have been proposed which combine these two methods, aiming to leverage the strengths and mitigate the weaknesses of both approaches. In this paper we introduce a new Evolutionary Reinforcement Learning model which combines a particular family of Evolutionary algorithm called Evolutionary Strategies with the off-policy Deep Reinforcement Learning algorithm TD3. The framework utilises a multi-buffer system instead of using a single shared replay buffer. The multi-buffer system allows for the Evolutionary Strategy to search freely in the search space of policies, without running the risk of overpopulating the replay buffer with poorly performing trajectories which limit the number of desirable policy behaviour examples thus negatively impacting the potential of the Deep Reinforcement Learning within the shared framework. The proposed algorithm is demonstrated to perform competitively with current Evolutionary Reinforcement Learning algorithms on MuJoCo control tasks, outperforming the well known state-of-the-art CEM-RL on 3 of the 4 environments tested., Comment: 9 pages, 4 figures, ALA 2023 workshop

Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However, it is often possible to observe the behavior of decision-makers. In multi-objective decision-making, preference inference is the process of inferring the preferences of a decision-maker for different objectives. This research proposes a Dynamic Weight-based Preference Inference (DWPI) algorithm that can infer the preferences of agents acting in multi-objective decision-making problems, based on observed behavior trajectories in the environment. The proposed method is evaluated on three multi-objective Markov decision processes: Deep Sea Treasure, Traffic, and Item Gathering. The performance of the proposed DWPI approach is compared to two existing preference inference methods from the literature, and empirical results demonstrate significant improvements compared to the baseline algorithms, in terms of both time requirements and accuracy of the inferred preferences. The Dynamic Weight-based Preference Inference algorithm also maintains its performance when inferring preferences for sub-optimal behavior demonstrations. In addition to its impressive performance, the Dynamic Weight-based Preference Inference algorithm does not require any interactions during training with the agent whose preferences are inferred, all that is required is a trajectory of observed behavior., Comment: This work is accepted by ALA 2023 Adaptive and Learning Agents Workshop at AAMAS, London, UK

Collecting, analyzing and gaining insight from large volumes of data is now the norm in an ever increasing number of industries. Data analytics techniques, such as machine learning, are powerful tools used to analyze these large volumes of data. Synthetic data sets are routinely relied upon to train and develop such data analytics methods for several reasons: to generate larger data sets than are available, to generate diverse data sets, to preserve anonymity in data sets with sensitive information, etc. Processing, transmitting and storing data is a key issue faced when handling large data sets. This paper presents an "On the fly" framework for generating big synthetic data sets, suitable for these data analytics methods, that is both computationally efficient and applicable to a diverse set of problems. An example application of the proposed framework is presented along with a mathematical analysis of its computational efficiency, demonstrating its effectiveness., Comment: 13 pages, 3 figures

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined., Comment: 17 pages, 3 figures