26 citations · 39 across the 21 of their papers we have counts for
8 papers · 1 filter
Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games
The Viet Bui, Tien Mai, Thanh Hong Nguyen
Training agents in multi-agent competitive games presents significant challenges due to their intricate nature. These challenges are exacerbated by dynamics influenced not only by…
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…
Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games
The Viet Bui, Tien Mai, Thanh H. Nguyen
Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim age…
Weighted Maximum Entropy Inverse Reinforcement Learning
The Viet Bui, Tien Mai, Patrick Jaillet
We study inverse reinforcement learning (IRL) and imitation learning (IM), the problems of recovering a reward or policy function from expert's demonstrated trajectories. We propos…
Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees
Avinandan Bose, Arunesh Sinha, Tien Mai
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO so…
Robust Entropy-regularized Markov Decision Processes
Tien Mai, Patrick Jaillet
Stochastic and soft optimal policies resulting from entropy-regularized Markov decision processes (ER-MDP) are desirable for exploration and imitation learning applications. Motiva…
Inverse Reinforcement Learning with Missing Data
Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low +1
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in…
Generalized Maximum Causal Entropy for Inverse Reinforcement Learning
Tien Mai, Kennard Chan, Patrick Jaillet
We consider the problem of learning from demonstrated trajectories with inverse reinforcement learning (IRL). Motivated by a limitation of the classical maximum entropy model in ca…