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Provable reward-agnostic preference-based reinforcement learning
Task-prompt generalised world model in multi-environment offline reinforcement learning
Offline reinforcement learning (RL) circumvents costly interactions with the environment by
utilising historical trajectories. Incorporating a world model into this method could …
utilising historical trajectories. Incorporating a world model into this method could …
Task aware dreamer for task generalization in reinforcement learning
A long-standing goal of reinforcement learning is to acquire agents that can learn on training
tasks and generalize well on unseen tasks that may share a similar dynamic but with …
tasks and generalize well on unseen tasks that may share a similar dynamic but with …
Model-Based Reinforcement Learning With Isolated Imaginations
World models learn the consequences of actions in vision-based interactive systems.
However, in practical scenarios like autonomous driving, noncontrollable dynamics that are …
However, in practical scenarios like autonomous driving, noncontrollable dynamics that are …
Transferable Reinforcement Learning via Generalized Occupancy Models
Intelligent agents must be generalists, capable of quickly adapting to various tasks. In
reinforcement learning (RL), model-based RL learns a dynamics model of the world, in …
reinforcement learning (RL), model-based RL learns a dynamics model of the world, in …
Filtered observations for model-based multi-agent reinforcement learning
Reinforcement learning (RL) pursues high sample efficiency in practical environments to
avoid costly interactions. Learning to plan with a world model in a compact latent space for …
avoid costly interactions. Learning to plan with a world model in a compact latent space for …
[PDF][PDF] World model architectures in reinforcement learning: an exploration of strengths and limitations
R Schiewer - 2024 - d-nb.info
Reinforcement learning (RL), a machine learning algorithm inspired by the trial-and-error
learning mechanism of natural intelligence, is characterised by agents that interact with their …
learning mechanism of natural intelligence, is characterised by agents that interact with their …
Enhancing robustness and interpretability in natural language processing through representation learning
H Yan - 2024 - wrap.warwick.ac.uk
Recent advancements in Natural Language Processing (NLP), particularly those driven by
representation learning, have led to significant breakthroughs in a range of applications …
representation learning, have led to significant breakthroughs in a range of applications …