activity
20022026
most citedReward Machines: Exploiting Reward Function Structure in Reinforcement Learning

188 citations · 291 across the 32 of their papers we have counts for

collaborators

37 papers

cs.LO2026

Progression- vs Automata-based Anticipatory Monitoring of LTL over Finite Traces (Extended Version)

Sarah Winkler, Toryn Klassen, Sheila McIlraith +1

When safety-critical systems are developed from a known internal specification, their correctness can be established by model checking. In the frequent case where such a specificat…

cs.AI2025

Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

Chandler Smith, Marwa Abdulhai, Manfred Diaz +83

Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…

cs.AI2025

Satisficing and Optimal Generalised Planning via Goal Regression (Extended Version)

Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen +1

Generalised planning (GP) refers to the task of synthesising programs that solve families of related planning problems. We introduce a novel, yet simple method for GP: given a set…

cs.AI2025

Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies

Dillon Z. Chen, Johannes Zenn, Tristan Cinquin +1

We study the usage of language models (LMs) for planning over world models specified in the Planning Domain Definition Language (PDDL). We prompt LMs to generate Python programs th…

cs.AI2025

Pushdown Reward Machines for Reinforcement Learning

Giovanni Varricchione, Toryn Q. Klassen, Natasha Alechina +3

Reward machines (RMs) are automata structures that encode (non-Markovian) reward functions for reinforcement learning (RL). RMs can reward any behaviour representable in regular la…

cs.LG2025

Better Training Data Attribution via Better Inverse Hessian-Vector Products

Andrew Wang, Elisa Nguyen, Runshi Yang +3

Training data attribution (TDA) provides insights into which training data is responsible for a learned model behavior. Gradient-based TDA methods such as influence functions and u…