354 citations · 856 across the 81 of their papers we have counts for
13 papers · 1 filter
Residual Q-Learning: Offline and Online Policy Customization without Value
Chenran Li, Chen Tang, Haruki Nishimura +3
Imitation Learning (IL) is a widely used framework for learning imitative behavior from demonstrations. It is especially appealing for solving complex real-world tasks where handcr…
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning
Ce Hao, Catherine Weaver, Chen Tang +3
Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward env…
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Zhixuan Liang, Yao Mu, Mingyu Ding +3
Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the…
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…
Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory
Ran Tian, Liting Sun, Masayoshi Tomizuka
Classical game-theoretic approaches for multi-agent systems in both the forward policy design problem and the inverse reward learning problem often make strong rationality assumpti…
In Proximity of ReLU DNN, PWA Function, and Explicit MPC
Saman Fahandezh-Saadi, Masayoshi Tomizuka
Rectifier (ReLU) deep neural networks (DNN) and their connection with piecewise affine (PWA) functions is analyzed. The paper is an effort to find and study the possibility of repr…