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Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback
Here we demonstrate how to use reinforcement learning from human feedback (RLHF) to improve upon simulated, embodied agents trained to a base level of competency with imitation learning.
Intra-agent speech permits zero-shot task acquisition
Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data.
Evaluating Multimodal Interactive Agents
Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research.
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language.
Imitating Interactive Intelligence
These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone.
Probing Emergent Semantics in Predictive Agents via Question Answering
Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments.
Optimizing Agent Behavior over Long Time Scales by Transporting Value
Humans spend a remarkable fraction of waking life engaged in acts of "mental time travel".
