6 papers · 1 filter
Learning What to Do and What Not To Do: Offline Imitation from Expert and Undesirable Demonstrations
Huy Hoang, Tien Mai, Pradeep Varakantham +1
Offline imitation learning typically learns from expert and unlabeled demonstrations, yet often overlooks the valuable signal in explicitly undesirable behaviors. In this work, we…
On Generalization Across Environments In Multi-Objective Reinforcement Learning
Jayden Teoh, Pradeep Varakantham, Peter Vamplew
Real-world sequential decision-making tasks often require balancing trade-offs between multiple conflicting objectives, making Multi-Objective Reinforcement Learning (MORL) an incr…
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Jayden Teoh, Wenjun Li, Pradeep Varakantham
Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent. The teacher generates new tr…
On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression
Zichang Ge, Changyu Chen, Arunesh Sinha +1
In real-world sequential decision making tasks like autonomous driving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitat…
UNIQ: Offline Inverse Q-learning for Avoiding Undesirable Demonstrations
Huy Hoang, Tien Mai, Pradeep Varakantham
We address the problem of offline learning a policy that avoids undesirable demonstrations. Unlike conventional offline imitation learning approaches that aim to imitate expert or…
Towards Neural Network based Cognitive Models of Dynamic Decision-Making by Humans
Changyu Chen, Shashank Reddy Chirra, Maria José Ferreira +3
Modeling human cognitive processes in dynamic decision-making tasks has been an endeavor in AI for a long time because such models can help make AI systems more intuitive, personal…