3 citations · 5 across the 2 of their papers we have counts for
5 papers · 1 filter
Generative Augmented Flow Networks
Ling Pan, Dinghuai Zhang, Aaron Courville +2
The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the probability of generating an object is propo…
Regularized Softmax Deep Multi-Agent -Learning
Ling Pan, Tabish Rashid, Bei Peng +2
Tackling overestimation in -learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little atte…
Multi-Path Policy Optimization
Ling Pan, Qingpeng Cai, Longbo Huang
Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, partic…
Deterministic Value-Policy Gradients
Qingpeng Cai, Ling Pan, Pingzhong Tang
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG alg…
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Ling Pan, Qingpeng Cai, Qi Meng +3
Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several bene…