Stimulus equivalence is a behavioural phenomenon in which participants demonstrate responding on contingencies not explicitly trained. Those stimuli are related in a equivalence class.
It's a special form o generalization where stimulus can control other contingencies without training.

Congratulations to Andrew Barto & Richard Sutton, who have won the 2024 ACM A.M. Turing Award for developing the conceptual & algorithmic foundations of reinforcement learning. An #openaccess edition of their notable text, "Reinforcement Learning," may be found here: mitpress.mit.edu/978026203924...

Trying to tell the story behind this explosion of research we are in. An unexpected RL Renaissance.
New talk! Forecasting the Alpaca moment for reasoning models and why the new style of RL training is a far bigger deal than the emergence of RLHF.
YouTube: https://buff.ly/41bVRPp

I am happy to announce that I successfully defended my PhD, entitled “Sample-Efficieny Multi-Task and Multi-Objective Reinforcement Learning by Combining Multiple Behaviors”! 🎉

These last years have been extremely fun, and I am very lucky to have collaborated with and met so many great people😄

Exciting news! My paper on multi-objective reinforcement learning was accepted at AAMAS 2025!

We introduce IPRO (Iterated Pareto Referent Optimisation)—a principled approach to solving multi-objective problems.

🔗 Paper: arxiv.org/abs/2402.07182
💻 Code: github.com/wilrop/ipro

Should be in every newsagents window.

An updated intro to reinforcement learning by Kevin Murphy: arxiv.org/abs/2412.05265! Like their books, it covers a lot and is quite up to date with modern approaches. It also is pretty unique in coverage, I don't think a lot of this is synthesized anywhere else yet

Can transformer-based models replicate the human ability to form stimulus equivalence?

Testing Stimulus Equivalence in Transformer-Based Agents.
www.mdpi.com/1999-5903/16...