research
∙
07/11/2023
Contextual Pre-Planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Recent studies show that deep reinforcement learning (DRL) agents tend t...
share
research
∙
05/23/2023
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
Reinforcement Learning (RL) environments can produce training data with ...
research
∙
05/09/2023
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning
There is a lack of standard benchmarks for Multi-Agent Reinforcement Lea...
research
∙
04/18/2023
Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
One of the key challenges of Reinforcement Learning (RL) is the ability ...
research
∙
02/23/2023
Revisiting the Gumbel-Softmax in MADDPG
MADDPG is an algorithm in multi-agent reinforcement learning (MARL) that...
research
∙
02/21/2023
Causal Social Explanations for Stochastic Sequential Multi-Agent Decision-Making
We present a novel framework to generate causal explanations for the dec...
research
∙
02/09/2023
Learning Complex Teamwork Tasks using a Sub-task Curriculum
Training a team to complete a complex task via multi-agent reinforcement...
research
∙
02/07/2023
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) requires agents to...
research
∙
12/22/2022
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
This project leverages advances in multi-agent reinforcement learning (M...
research
∙
10/26/2022
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
Reasoning with occluded traffic agents is a significant open challenge f...
research
∙
10/12/2022
DiPA: Diverse and Probabilistically Accurate Interactive Prediction
Accurate prediction is important for operating an autonomous vehicle in ...
research
∙
10/11/2022
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
Open ad hoc teamwork is the problem of training a single agent to effici...
research
∙
09/28/2022
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
Equilibrium selection in multi-agent games refers to the problem of sele...
research
∙
08/02/2022
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agent...
research
∙
07/29/2022
Perspectives on the System-level Design of a Safe Autonomous Driving Stack
Achieving safe and robust autonomy is the key bottleneck on the path tow...
research
∙
07/28/2022
Towards Robust Ad Hoc Teamwork Agents By Creating Diverse Training Teammates
Ad hoc teamwork (AHT) is the problem of creating an agent that must coll...
research
∙
07/19/2022
Few-Shot Teamwork
We propose the novel few-shot teamwork (FST) problem, where skilled agen...
research
∙
07/15/2022
Cooperative Marine Operations via Ad Hoc Teams
While research in ad hoc teamwork has great potential for solving real-w...
research
∙
07/12/2022
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
In real-world robotics applications, Reinforcement Learning (RL) agents ...
research
∙
07/05/2022
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
Successful deployment of multi-agent reinforcement learning often requir...
research
∙
06/28/2022
Verifiable Goal Recognition for Autonomous Driving with Occlusions
When used in autonomous driving, goal recognition allows the future beha...
research
∙
06/22/2022
Learning Representations for Control with Hierarchical Forward Models
Learning control from pixels is difficult for reinforcement learning (RL...
research
∙
06/17/2022
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning
Inscrutable AI systems are difficult to trust, especially if they operat...
research
∙
05/17/2022
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments
We present MIDGARD, an open source simulation platform for autonomous ro...
research
∙
03/15/2022
Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles
Motion prediction of road users in traffic scenes is critical for autono...
research
∙
02/16/2022
A Survey of Ad Hoc Teamwork: Definitions, Methods, and Open Problems
Ad hoc teamwork is the well-established research problem of designing ag...
research
∙
11/29/2021
Robust On-Policy Data Collection for Data-Efficient Policy Evaluation
This paper considers how to complement offline reinforcement learning (R...
research
∙
10/11/2021
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Deep reinforcement learning (RL) agents that exist in high-dimensional s...
research
∙
08/05/2021
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
Recognising the goals or intentions of observed vehicles is a key step t...
research
∙
07/19/2021
Decoupling Exploration and Exploitation in Reinforcement Learning
Intrinsic rewards are commonly applied to improve exploration in reinfor...
research
∙
03/10/2021
GRIT: Verifiable Goal Recognition for Autonomous Driving using Decision Trees
It is useful for autonomous vehicles to have the ability to infer the go...
research
∙
02/15/2021
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Sharing parameters in multi-agent deep reinforcement learning has played...
research
∙
07/18/2020
Quantum-Secure Authentication via Abstract Multi-Agent Interaction
Current methods for authentication based on public-key cryptography are ...
research
∙
06/18/2020
Open Ad Hoc Teamwork using Graph-based Policy Learning
Ad hoc teamwork is the challenging problem of designing an autonomous ag...
research
∙
06/16/2020
Local Information Opponent Modelling Using Variational Autoencoders
Modelling the behaviours of other agents (opponents) is essential for un...
research
∙
06/14/2020
Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms
Multi-agent deep reinforcement learning (MARL) suffers from a lack of co...
research
∙
06/12/2020
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Exploration in multi-agent reinforcement learning is a challenging probl...
research
∙
02/06/2020
Integrating Planning and Interpretable Goal Recognition for Autonomous Driving
The ability to predict the intentions and driving trajectories of other ...
research
∙
02/06/2020
A Two-Stage Optimization Approach to Safe-by-Design Planning for Autonomous Driving
Lessons learned from the increasing diversity of road trial deployments ...
research
∙
01/29/2020
Variational Autoencoders for Opponent Modeling in Multi-Agent Systems
Multi-agent systems exhibit complex behaviors that emanate from the inte...
research
∙
09/30/2019
Stabilizing Generative Adversarial Network Training: A Survey
Generative Adversarial Networks (GANs) are a type of Generative Models, ...
research
∙
07/23/2019
E-HBA: Using Action Policies for Expert Advice and Agent Typification
Past research has studied two approaches to utilise predefined policy se...
research
∙
07/22/2019
Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems
This paper is concerned with evaluating different multiagent learning (M...
research
∙
07/15/2019
On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
While many multiagent algorithms are designed for homogeneous systems (i...
research
∙
07/10/2019
An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types
Many multiagent applications require an agent to learn quickly how to in...
research
∙
07/10/2019
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...
research
∙
07/02/2019
Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
The key for effective interaction in many multiagent applications is to ...
research
∙
06/26/2019
Reasoning about Hypothetical Agent Behaviours and their Parameters
Agents can achieve effective interaction with previously unknown other a...
research
∙
06/11/2019
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
Recent developments in deep reinforcement learning are concerned with cr...
research
∙
01/10/2018
