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Found 11 papers, 4 papers with code
DeepMind Control Suite
The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents.
Learning Awareness Models
We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.
Distributed Distributional Deterministic Policy Gradients
This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting.
dm_control: Software and Tasks for Continuous Control
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available.
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.
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.
A data-driven approach for learning to control computers
It would be useful for machines to use computers as humans do so that they can aid us in everyday tasks.
Evaluating Multimodal Interactive Agents
Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research.
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.
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.
