Causality in Physics and Effective Theories of Agency

Causal Learning Workshop at NeurIPS


We propose to combine reinforcement learning and theoretical physics to describe effective theories of agency. This involves understanding the connection between the physics notion of causality and how intelligent agents can arise as a useful effective description within some environments. We discuss cases where such an effective theory of agency can break down and suggest a broader framework incorporating theory of mind for expanding the notion of agency in the presence of other agents that can predict actions. We comment on implications for superintelligence and whether physical bounds can be used to place limits on such predictors.

Related Publications

All Publications

EMNLP - October 1, 2021

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little

Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, Douwe Kiela

IROS - September 30, 2021

Learning Navigation Skills for Legged Robots with Learned Robot Embeddings

Joanne Truong, Denis Yarats, Tianyu Li, Franziska Meier, Sonia Chernova, Dhruv Batra, Akshara Rai

IROS - September 27, 2021

Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning

Kalyan Vasudev Alwala, Mustafa Mukadam

RecSys - September 27, 2021

Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation

Gabriel De Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, Even Oldridge

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy