3 papers
cs.LG2026
Anticipating the Consequences of Curriculum Decisions with Large Language Models
Octavio Pappalardo, Nathan Herr, Tim Rocktäschel
Automatic curriculum learning can improve the effectiveness of reinforcement learning by selecting the training experiences presented to the agent over time. Predicting the consequ…
cs.LG2026
Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals
Octavio Pappalardo
Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks. A promising direction, grounded in human develop…
cs.LG2024
Black Box Meta-Learning Intrinsic Rewards
Octavio Pappalardo, Rodrigo Ramele, Juan Miguel Santos
The broader application of reinforcement learning (RL) is limited by challenges including data efficiency, generalization capability, and ability to learn in sparse-reward environm…