12 citations · 17 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
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…
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 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…
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…