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Reinforcement Learning
Finding the FrameStack: Learning What to Remember for Non-Markovian Reinforcement Learning
Recent success in developing increasingly general purpose agents based on sequence models has led to increased focus on the problem of …
Geraud Nangue Tasse
,
Matthew Riemer
,
Benjamin Rosman
,
Tim Klinger
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Procedural Generation of Semantically Correct Levels in Video Games using Reward Shaping
The generation of video game levels traditionally relies on manual efforts from skilled professionals, resulting in significant …
Luke Kerker
,
Branden Ingram
,
Pravesh Ranchod
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M-SAT: Multi-State-Action Tokenisation in Decision Transformers for Multi-Discrete Actions
Effective decision-making in complex environments with multi-discrete action spaces poses significant challenges for agent …
Perusha Moodley
,
Dhillu Thambi
,
Mark Trovinger
,
Pramod Kaushik
,
Praveen Paruchuri
,
Xia Hong
,
Benjamin Rosman
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Using NEAT to Learn Operators for Flexible Boolean Composition within Reinforcement Learning
Skill composition is a growing area of interest within Reinforcement Learning (RL) research. For example, if designing a robot for …
Amir Esterhuysen
,
Steven James
,
Geraud Nangue Tasse
,
Benjamin Rosman
,
Jonathan Shock
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Composition and Zero-Shot Transfer with Lattice Structures in Reinforcement Learning
An important property of long-lived agents is the ability to reuse existing knowledge to solve new tasks. An appealing approach towards …
Geraud Nangue Tasse
,
Steven James
,
Benjamin Rosman
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Project
DOI
Compositional Instruction Following with Language Models and Reinforcement Learning
Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while …
Vanya Cohen
,
Geraud Nangue Tasse
,
Nakul Gopalan
,
Steven James
,
Matthew Gombolay
,
Ray Mooney
,
Benjamin Rosman
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Optimal Task Generalisation in Cooperative Multi-Agent Reinforcement Learning
While task generalisation is widely studied in the context of single-agent reinforcement learning (RL), little research exists in the …
Simon Rosen
,
Abdel Mfougouon Njupoun
,
Geraud Nangue Tasse
,
Steven James
,
Benjamin Rosman
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Project
ROSARL: Reward-Only Safe Reinforcement Learning
An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common …
Geraud Nangue Tasse
,
Tamlin Love
,
Mark Nemecek
,
Steven James
,
Benjamin Rosman
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Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
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 …
Geraud Nangue Tasse
,
Devon Jarvis
,
Steven James
,
Benjamin Rosman
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Project
Transferable Dynamics Models for Efficient Object-Oriented Reinforcement Learning
The Reinforcement Learning (RL) framework offers a general paradigm for constructing autonomous agents that can make effective …
Ofir Marom
,
Benjamin Rosman
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DOI
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