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Found 25 papers, 5 papers with code
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
In this work, we propose ACTER (Actionable Counterfactual Sequences for Explaining Reinforcement Learning Outcomes), an algorithm for generating counterfactual sequences that provides actionable advice on how failure can be avoided.
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration.
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks
Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles.
Reservation of Virtualized Resources with Optimistic Online Learning
The different service providers (SPs) have the opportunity to lease the network resources from the NO to constitute slices that address the demand of their specific network service.
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning
In this work, we propose RACCER, the first RL-specific approach to generating counterfactual explanations for the behavior of RL agents.
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning
As an alternative, in this paper we propose Expert-Free Online Transfer Learning (EF-OnTL), an algorithm that enables expert-free real-time dynamic transfer learning in multi-agent system.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning
In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective tabular Reinforcement Learning (RL) algorithm W-learning to large state spaces.
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
We apply CausalCF to complex robotic tasks and show that it improves the RL agent's robustness using CausalWorld.
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities
Additionally, we explore the differences between counterfactual explanations in supervised learning and RL and identify the main challenges that prevent the adoption of methods from supervised in reinforcement learning.
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context.
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing
This paper proposes the Federated Simulated Annealing (FedSA) metaheuristic to select the hyperparameters and a subset of participants for each aggregation round in federated learning.
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning
We propose ReCCoVER, an algorithm which detects causal confusion in agent's reasoning before deployment, by executing its policy in alternative environments where certain correlations between features do not hold.
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems
Reinforcement learning (RL) has been used in a range of simulated real-world tasks, e. g., sensor coordination, traffic light control, and on-demand mobility services.
Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points
In this paper, we propose a multi-agent deep reinforcement learning approach to optimise the energy efficiency of fixed-wing UAV cellular access points while still allowing them to deliver high-quality service to users on the ground.
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT
As such, power-control mechanisms and intelligent mobility of the relay devices are critical in improving communication performance and energy utilization.
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning
Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to provide wireless connectivity to ground devices in events of increased network demand, points-of-failure in existing infrastructure, or disasters.
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options
Options are explored in interaction with the environment, and the most suitable options for each context are used to generate adaptations exploiting COP.
Adaptation to Unknown Situations as the Holy Grail of Learning-Based Self-Adaptive Systems: Research Directions
Capturing all possible changes to define suitable behaviour beforehand is unfeasible, or even impossible in the case of unknown changes, hence human intervention may be required.
Mobility for Cellular-Connected UAVs: challenges for the network provider
Unmanned Aerial Vehicle (UAV) technology is becoming more prevalent and more diverse in its application.
Networking and Internet Architecture
Experimental Evaluation of a UAV User QoS from a Two-Tier 3.6GHz Spectrum Network
Our results show that increasing the UAV height reduces the performance in both tiers, due to issues such as antenna misalignment.
Networking and Internet Architecture
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning
On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.
Multi-agent Deep Reinforcement Learning for Zero Energy Communities
This paper addresses the problem of energy sharing in such a community.
Decentralised Multi-Agent Reinforcement Learning for Dynamic and Uncertain Environments
In this paper we propose P-MARL, a decentralised MARL algorithm enhanced by a prediction mechanism that provides accurate information regarding up-coming changes in the environment.
Multiagent Systems
