5 citations · 11 across the 5 of their papers we have counts for
5 papers
Reparameterized Policy Learning for Multimodal Trajectory Optimization
Zhiao Huang, Litian Liang, Zhan Ling +3
We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy…
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
Litian Liang, Yaosheng Xu, Stephen McAleer +4
In temporal-difference reinforcement learning algorithms, variance in value estimation can cause instability and overestimation of the maximal target value. Many algorithms have be…
Target Entropy Annealing for Discrete Soft Actor-Critic
Yaosheng Xu, Dailin Hu, Litian Liang +3
Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in continuous action space settings. It uses the maximum entropy framework for efficiency and stability, and ap…
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Litian Liang, Yaosheng Xu, Stephen McAleer +4
Temporal-Difference (TD) learning methods, such as Q-Learning, have proven effective at learning a policy to perform control tasks. One issue with methods like Q-Learning is that t…
Modular Framework for Visuomotor Language Grounding
Kolby Nottingham, Litian Liang, Daeyun Shin +3
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. However, data collection for these tasks is expensive and end…