424 citations · 860 across the 53 of their papers we have counts for
55 papers
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Guy Azran, Mohamad H. Danesh, Stefano V. Albrecht +1
Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite…
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning
Adam Michalski, Filippos Christianos, Stefano V. Albrecht
There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning (MARL) algorithms. The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, b…
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck +2
Reinforcement Learning (RL) environments can produce training data with spurious correlations between features due to the amount of training data or its limited feature coverage. T…
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments
Alain Andres, Lukas Schäfer, Stefano V. Albrecht +1
One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large…
Revisiting the Gumbel-Softmax in MADDPG
Callum Rhys Tilbury, Filippos Christianos, Stefano V. Albrecht
MADDPG is an algorithm in multi-agent reinforcement learning (MARL) that extends the popular single-agent method, DDPG, to multi-agent scenarios. Importantly, DDPG is an algorithm…
Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
Balint Gyevnar, Cheng Wang, Christopher G. Lucas +2
We present CEMA: Causal Explanations in Multi-Agent systems; a framework for creating causal natural language explanations of an agent's decisions in dynamic sequential multi-agent…
Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
Elliot Fosong, Arrasy Rahman, Ignacio Carlucho +1
Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-st…
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Lukas Schäfer, Oliver Slumbers, Stephen McAleer +3
Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL alg…
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas +8
We consider a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, calle…
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
Filippos Christianos, Peter Karkus, Boris Ivanovic +2
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predictin…
DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht +2
Accurate prediction is important for operating an autonomous vehicle in interactive scenarios. Prediction must be fast, to support multiple requests from a planner exploring a rang…
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
Arrasy Rahman, Ignacio Carlucho, Niklas Höpner +1
Open ad hoc teamwork is the problem of training a single agent to efficiently collaborate with an unknown group of teammates whose composition may change over time. A variable team…
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
Filippos Christianos, Georgios Papoudakis, Stefano V. Albrecht
This work focuses on equilibrium selection in no-conflict multi-agent games, where we specifically study the problem of selecting a Pareto-optimal Nash equilibrium among several ex…
Deep Reinforcement Learning for Multi-Agent Interaction
Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho +14
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Tow…
Perspectives on the System-level Design of a Safe Autonomous Driving Stack
Majd Hawasly, Jonathan Sadeghi, Morris Antonello +3
Achieving safe and robust autonomy is the key bottleneck on the path towards broader adoption of autonomous vehicles technology. This motivates going beyond extrinsic metrics such…
Few-Shot Teamwork
Elliot Fosong, Arrasy Rahman, Ignacio Carlucho +1
We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and togethe…
Cooperative Marine Operations via Ad Hoc Teams
Ignacio Carlucho, Arrasy Rahman, William Ard +3
While research in ad hoc teamwork has great potential for solving real-world robotic applications, most developments so far have been focusing on environments with simple dynamics.…
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
Arrasy Rahman, Elliot Fosong, Ignacio Carlucho +1
Ad hoc teamwork (AHT) is the challenge of designing a robust learner agent that effectively collaborates with unknown teammates without prior coordination mechanisms. Early approac…
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck +2
Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially…
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
Lukas Schäfer, Filippos Christianos, Amos Storkey +1
Successful deployment of multi-agent reinforcement learning often requires agents to adapt their behaviour. In this work, we discuss the problem of teamwork adaptation in which a t…
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning
Balint Gyevnar, Massimiliano Tamborski, Cheng Wang +3
Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and q…
Verifiable Goal Recognition for Autonomous Driving with Occlusions
Cillian Brewitt, Massimiliano Tamborski, Cheng Wang +1
Goal recognition (GR) involves inferring the goals of other vehicles, such as a certain junction exit, which can enable more accurate prediction of their future behaviour. In auton…
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined. Previous approaches remedy thi…
Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles
Morris Antonello, Mihai Dobre, Stefano V. Albrecht +2
Motion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a…