10 papers
On Discovering Algorithms for Adversarial Imitation Learning
Shashank Reddy Chirra, Jayden Teoh, Praveen Paruchuri +1
Adversarial Imitation Learning (AIL) methods, while effective in settings with limited expert demonstrations, are often considered unstable. These approaches typically decompose in…
Efficient Unsupervised Environment Design through Hierarchical Policy Representation Learning
Dexun Li, Sidney Tio, Pradeep Varakantham
Unsupervised Environment Design (UED) has emerged as a promising approach to developing general-purpose agents through automated curriculum generation. Popular UED methods focus on…
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…
Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning
Wenjun Li, Changyu Chen, Pradeep Varakantham
Large language models (LLMs) have demonstrated impressive task-solving capabilities through prompting techniques and system designs, including solving planning tasks (e.g., math pr…
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…
Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility
Hao Jiang, Yixing Xu, Pradeep Varakantham
The Ride-Pool Matching Problem (RMP) is central to on-demand ride-pooling services, where vehicles must be matched with multiple requests while adhering to service constraints such…