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Foundation policies with hilbert representations
The power of resets in online reinforcement learning
Simulators are a pervasive tool in reinforcement learning, but most existing algorithms
cannot efficiently exploit simulator access--particularly in high-dimensional domains that …
cannot efficiently exploit simulator access--particularly in high-dimensional domains that …
Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Real-world applications of reinforcement learning often involve environments where agents
operate on complex, high-dimensional observations, but the underlying (``latent'') dynamics …
operate on complex, high-dimensional observations, but the underlying (``latent'') dynamics …
Learning latent dynamic robust representations for world models
Visual Model-Based Reinforcement Learning (MBRL) promises to encapsulate agent's
knowledge about the underlying dynamics of the environment, enabling learning a world …
knowledge about the underlying dynamics of the environment, enabling learning a world …
Leveraging Separated World Model for Exploration in Visually Distracted Environments
Abstract Model-based unsupervised reinforcement learning (URL) has gained prominence
for reducing environment interactions and learning general skills using intrinsic rewards …
for reducing environment interactions and learning general skills using intrinsic rewards …
Masked and Inverse Dynamics Modeling for Data-Efficient Reinforcement Learning
In pixel-based deep reinforcement learning (DRL), learning representations of states that
change because of an agent's action or interaction with the environment poses a critical …
change because of an agent's action or interaction with the environment poses a critical …
Policy-shaped prediction: avoiding distractions in model-based reinforcement learning
Abstract Model-based reinforcement learning (MBRL) is a promising route to sample-
efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL …
efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL …
Learning Abstract World Model for Value-preserving Planning with Options
General-purpose agents require fine-grained controls and rich sensory inputs to perform a
wide range of tasks. However, this complexity often leads to intractable decision-making …
wide range of tasks. However, this complexity often leads to intractable decision-making …
Mastering robot manipulation with multimodal prompts through pretraining and multi-task fine-tuning
Prompt-based learning has been demonstrated as a compelling paradigm contributing to
large language models' tremendous success (LLMs). Inspired by their success in language …
large language models' tremendous success (LLMs). Inspired by their success in language …