11 papers
Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning
Meraj Mammadov, Pedro Zuidberg Dos Martires, Johannes Andreas Stork
Imitation learning (IL) from a state-based reinforcement learning (RL) policy is a common approach to overcome the curse of dimensionality in complex and high-dimensional observati…
COvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game
Alkis Sygkounas, Rishi Hazra, Andreas Persson +2
A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and gener…
Two Constraint Compilation Methods for Lifted Planning
Periklis Mantenoglou, Luigi Bonassi, Enrico Scala +1
We study planning in a fragment of PDDL with qualitative state-trajectory constraints, capturing safety requirements, task ordering conditions, and intermediate sub-goals commonly…
APC-RL: Exceeding Data-Driven Behavior Priors with Adaptive Policy Composition
Finn Rietz, Pedro Zuidberg dos Martires, Johannes Andreas Stork
Incorporating demonstration data into reinforcement learning (RL) can greatly accelerate learning, but existing approaches often assume demonstrations are optimal and fully aligned…
LexiCon: a Benchmark for Planning under Temporal Constraints in Natural Language
Periklis Mantenoglou, Rishi Hazra, Pedro Zuidberg Dos Martires +1
Owing to their reasoning capabilities, large language models (LLMs) have been evaluated on planning tasks described in natural language. However, LLMs have largely been tested on p…
A Quantum Information Theoretic Approach to Tractable Probabilistic Models
Pedro Zuidberg Dos Martires
By recursively nesting sums and products, probabilistic circuits have emerged in recent years as an attractive class of generative models as they enjoy, for instance, polytime marg…