35 citations · 89 across the 43 of their papers we have counts for
12 papers · 1 filter
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…
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
Huy Hoang, Tien Mai, Pradeep Varakantham
A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are em…
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
Changyu Chen, Ramesha Karunasena, Thanh Hong Nguyen +2
Many problems in Reinforcement Learning (RL) seek an optimal policy with large discrete multidimensional yet unordered action spaces; these include problems in randomized allocatio…
Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling
Dexun Li, Pradeep Varakantham
Unsupervised Environment Design (UED) is a paradigm for automatically generating a curriculum of training environments, enabling agents trained in these environments to develop gen…
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…
Strategic Planning for Flexible Agent Availability in Large Taxi Fleets
Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng
In large-scale multi-agent systems like taxi fleets, individual agents (taxi drivers) are self-interested (maximizing their own profits) and this can introduce inefficiencies in th…