In the spirit of a technology developed by AI company Anthropic, Microsoft sees the future of AI where there are lots of ...

This study provides a valuable extension of credibility-based learning research by showing how feedback reliability can distort reward-learning biases in a disinformation-like bandit task. Although ...

Transfer Learning for Reinforcement Learning Domains: A Survey. Matthew E. Taylor and Peter Stone. Journal of Machine Learning Research, 10(1):1633–1685, 2009.

Overall, the data support the conclusion that negative reinforcement, not just positive reinforcement, is an important factor in the perpetuation of substance use and suggests that learning to use ...

Mount Sinai researchers have identified for the first time the neural mechanisms in the brain that regulate both positive and negative impressions ... new memories, learning, and emotions.

and reinforcement. Every comment, rubric, and deadline form part of that environment. When negative consequences are off the table, as with AI, we instinctively do what behaviorists have ...

Researchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning.