Olivier Sigaud

7

Papers

4

Total Citations

Papers (7)

MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces

Bridging Environments and Language with Rendering Functions and Vision-Language Models

Learning Compositional Neural Programs with Recursive Tree Search and Planning

EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL

Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments

GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning