411 citations · 615 across the 21 of their papers we have counts for
10 papers · 1 filter
Transformer in Transformer as Backbone for Deep Reinforcement Learning
Hangyu Mao, Rui Zhao, Hao Chen +5
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network…
Auto-Encoding Adversarial Imitation Learning
Kaifeng Zhang, Rui Zhao, Ziming Zhang +1
Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imit…
Mutual Information State Intrinsic Control
Rui Zhao, Yang Gao, Pieter Abbeel +2
Reinforcement learning has been shown to be highly successful at many challenging tasks. However, success heavily relies on well-shaped rewards. Intrinsically motivated RL attempts…
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Zhiliang Wu, Yinchong Yang, Yunpu Ma +4
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electron…
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning
Rui Zhao, Yang Gao, Pieter Abbeel +2
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal. In the natural world, intelligent organisms learn from internal drives, by…
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Rui Zhao, Xudong Sun, Volker Tresp
In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a re…
Neural Networks for Modeling Source Code Edits
Rui Zhao, David Bieber, Kevin Swersky +1
Programming languages are emerging as a challenging and interesting domain for machine learning. A core task, which has received significant attention in recent years, is building…
Curiosity-Driven Experience Prioritization via Density Estimation
Rui Zhao, Volker Tresp
In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning. However, the collected trajectories can easil…
Energy-Based Hindsight Experience Prioritization
Rui Zhao, Volker Tresp
In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was…
Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
Rui Zhao, Volker Tresp
Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end…