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Found 3 papers, 1 papers with code
Provably Sample-Efficient RL with Side Information about Latent Dynamics
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a robot is tasked to go to a specific room in a building using observations from its own camera, while having access to the floor plan.
Interactive Learning from Activity Description
We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities.
General Reinforcement Learning
Grounded language learning
+2
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention.
