35 citations · 89 across the 43 of their papers we have counts for
5 papers · 2 filters
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…
Regret-Based Defense in Adversarial Reinforcement Learning
Roman Belaire, Pradeep Varakantham, Thanh Nguyen +1
Deep Reinforcement Learning (DRL) policies have been shown to be vulnerable to small adversarial noise in observations. Such adversarial noise can have disastrous consequences in s…
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties
Hao Jiang, Tien Mai, Pradeep Varakantham +1
Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints. The key challenge is in handling expected…