35 citations · 89 across the 43 of their papers we have counts for
6 papers · 2 filters
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators
Sidney Tio, Jimmy Ho, Pradeep Varakantham
We adapt Parameterized Environment Response Model (PERM), a method for training both Reinforcement Learning (RL) Agents and human learners in parameterized environments by directly…
Transferable Curricula through Difficulty Conditioned Generators
Sidney Tio, Pradeep Varakantham
Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial…
Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning
Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily sh…
Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling
Xianjie Zhang, Pradeep Varakantham, Hao Jiang
The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to few…
Diversity Induced Environment Design via Self-Play
Dexun Li, Wenjun Li, Pradeep Varakantham
Recent work on designing an appropriate distribution of environments has shown promise for training effective generally capable agents. Its success is partly because of a form of a…
Generalization through Diversity: Improving Unsupervised Environment Design
Wenjun Li, Pradeep Varakantham, Dexun Li
Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e.g., moving in an 8x8 maze with three rooms, playing Ches…