12 citations · 50 across the 15 of their papers we have counts for
4 papers · 1 filter
PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
Lingfeng Sun, Haichao Zhang, Wei Xu +1
The purpose of multi-task reinforcement learning (MTRL) is to train a single policy that can be applied to a set of different tasks. Sharing parameters allows us to take advantage…
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
Haichao Zhang, Wei Xu, Haonan Yu
Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return.…
Do You Need the Entropy Reward (in Practice)?
Haonan Yu, Haichao Zhang, Wei Xu
Maximum entropy (MaxEnt) RL maximizes a combination of the original task reward and an entropy reward. It is believed that the regularization imposed by entropy, on both policy imp…
Towards Safe Reinforcement Learning with a Safety Editor Policy
Haonan Yu, Wei Xu, Haichao Zhang
We consider the safe reinforcement learning (RL) problem of maximizing utility with extremely low constraint violation rates. Assuming no prior knowledge or pre-training of the env…