12 citations · 17 across the 15 of their papers we have counts for
15 papers
Redistribution-based Cost Inference Improves Sparse Safe Offline RL
Ebenezer Gelo, Geraud Nangue Tasse, Steven James +1
Safe offline RL typically assumes access to dense per-step cost annotations, but in practice supervisors provide only trajectory-level stop-feedback: a binary signal at the first u…
CORDA: A Benchmark for Hierarchical Harm-Centric Moral Reasoning in Large Language Models
Siddarth Singh, Victoria Williams, Simon Rosen +6
The key question in moral judgement is not simply whether someone chooses the "right" answer, but how they decide what matters most when moral principles conflict. Current evaluati…
MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents
Simon Rosen, Siddarth Singh, Ebenezer Gelo +6
Evaluating moral alignment in agents navigating conflicting, hierarchically structured human norms is a critical challenge at the intersection of AI safety, moral philosophy, and c…
Unsupervised Hierarchical Skill Discovery
Damion Harvey, Geraud Nangue Tasse, Benjamin Rosman +2
We consider the problem of unsupervised skill segmentation and hierarchical structure discovery in reinforcement learning. While recent approaches have sought to segment trajectori…
Beyond Sliding Windows: Learning to Manage Memory in Non-Markovian Environments
Geraud Nangue Tasse, Matthew Riemer, Benjamin Rosman +1
Recent success in developing increasingly general purpose agents based on sequence models has led to increased focus on the problem of deploying computationally limited agents with…
Compositional Instruction Following with Language Models and Reinforcement Learning
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +4
Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while simultaneously learning multiple language-conditioned ta…
RobocupGym: A challenging continuous control benchmark in Robocup
Michael Beukman, Branden Ingram, Geraud Nangue Tasse +2
Reinforcement learning (RL) has progressed substantially over the past decade, with much of this progress being driven by benchmarks. Many benchmarks are focused on video or board…
Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure
Tristan Bester, Benjamin Rosman, Steven James +1
We present counting reward automata-a finite state machine variant capable of modelling any reward function expressible as a formal language. Unlike previous approaches, which are…
ROSARL: Reward-Only Safe Reinforcement Learning
Geraud Nangue Tasse, Tamlin Love, Mark Nemecek +2
An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common solution is for a human expert to define either a pe…
World Value Functions: Knowledge Representation for Learning and Planning
Geraud Nangue Tasse, Benjamin Rosman, Steven James
We propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an a…
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
Geraud Nangue Tasse, Steven James, Benjamin Rosman
An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to solve multiple tasks in a given world. In th…
Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
Geraud Nangue Tasse, Devon Jarvis, Steven James +1
It is desirable for an agent to be able to solve a rich variety of problems that can be specified through language in the same environment. A popular approach towards obtaining suc…
Hierarchical Reinforcement Learning with AI Planning Models
Junkyu Lee, Michael Katz, Don Joven Agravante +4
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy to integrat…
Learning to Follow Language Instructions with Compositional Policies
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +3
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions. Ou…
A Boolean Task Algebra for Reinforcement Learning
Geraud Nangue Tasse, Steven James, Benjamin Rosman
The ability to compose learned skills to solve new tasks is an important property of lifelong-learning agents. In this work, we formalise the logical composition of tasks as a Bool…