Author name cluster

Christine Evers

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICML Conference 2025 Conference Paper

Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC

Tyler Clark
Mark Towers
Christine Evers
Jonathon Hare

Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent’s performance. In this paper, we present “Beyond The Rainbow” (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized interquartile mean (IQM) of 7. 6 on Atari-60. Beyond Atari, we demonstrate BTR’s capability to handle complex 3D games, successfully training agents to play Super Mario Galaxy, Mario Kart, and Mortal Kombat with minimal algorithmic changes. Designing BTR with computational efficiency in mind, agents can be trained using a high-end desktop PC on 200 million Atari frames within 12 hours. Additionally, we conduct detailed ablation studies of each component, analyzing the performance and impact using numerous measures.

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AAAI Conference 2025 Conference Paper

ME: Modelling Ethical Values for Value Alignment

Eryn Rigley
Adriane Chapman
Christine Evers
Will McNeill

Value alignment, at the intersection of moral philosophy and AI safety, is dedicated to ensuring that artificially intelligent (AI) systems align with a certain set of values. One challenge facing value alignment researchers is accurately translating these values into a machine readable format. In the case of reinforcement learning (RL), a popular method within value alignment, this requires designing a reward function which accurately defines the value of all state-action pairs. It is common for programmers to hand-set and manually tune these values. In this paper, we examine the challenges of hand-programming values into reward functions for value alignment, and propose mathematical models as an alternative grounding for reward function design in ethical scenarios. Experimental results demonstrate that our modelled-ethics approach offers a more consistent alternative and outperforms our hand-programmed reward functions.

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ICLR Conference 2022 Conference Paper

Model Agnostic Interpretability for Multiple Instance Learning

Joseph Early
Christine Evers
Sarvapali D. Ramchurn

In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.

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NeurIPS Conference 2022 Conference Paper

Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning

Joseph Early
Tom Bewley
Christine Evers
Sarvapali Ramchurn

We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independently when providing feedback on agent behaviour. In this work, we remove this assumption, extending RM to capture temporal dependencies in human assessment of trajectories. We show how RM can be approached as a multiple instance learning (MIL) problem, where trajectories are treated as bags with return labels, and steps within the trajectories are instances with unseen reward labels. We go on to develop new MIL models that are able to capture the time dependencies in labelled trajectories. We demonstrate on a range of RL tasks that our novel MIL models can reconstruct reward functions to a high level of accuracy, and can be used to train high-performing agent policies.

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