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Found 9 papers, 1 papers with code
Hindsight PRIORs for Reward Learning from Human Preferences
Incorporating state importance into reward learning improves the speed of policy learning, overall policy performance, and reward recovery on both locomotion and manipulation tasks.
Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors.
Large Language Models as Generalizable Policies for Embodied Tasks
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks.
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning
In this work, we demonstrate that encoding environment dynamics in the reward function (REED) dramatically reduces the number of preference labels required in state-of-the-art preference-based RL frameworks.
Symbol Guided Hindsight Priors for Reward Learning from Human Preferences
Specifying rewards for reinforcement learned (RL) agents is challenging.
On the role of Lip Articulation in Visual Speech Perception
Previous research has shown that traditional metrics used to optimize and assess models for generating lip motion from speech are not a good indicator of subjective opinion of animation quality.
FedEmbed: Personalized Private Federated Learning
Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical.
Mirroring to Build Trust in Digital Assistants
We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user.
Learning Sharing Behaviors with Arbitrary Numbers of Agents
We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of agents.
