11 papers
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…
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…
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…
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…
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…
Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs
Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, pr…
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…
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…