Cross-domain policy adaptation via value-guided data filtering
Generalizing policies across different domains with dynamics mismatch poses a significant
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …
Generalization to new sequential decision making tasks with in-context learning
Training autonomous agents that can learn new tasks from only a handful of demonstrations
is a long-standing problem in machine learning. Recently, transformers have been shown to …
is a long-standing problem in machine learning. Recently, transformers have been shown to …
Improving generalization in meta-rl with imaginary tasks from latent dynamics mixture
The generalization ability of most meta-reinforcement learning (meta-RL) methods is largely
limited to test tasks that are sampled from the same distribution used to sample training …
limited to test tasks that are sampled from the same distribution used to sample training …
An Information-Assisted Deep Reinforcement Learning Path Planning Scheme for Dynamic and Unknown Underwater Environment
An autonomous underwater vehicle (AUV) has shown impressive potential and promising
exploitation prospects in numerous marine missions. Among its various applications, the …
exploitation prospects in numerous marine missions. Among its various applications, the …
Neural stochastic dual dynamic programming
Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-
stage stochastic optimization, widely used for modeling real-world process optimization …
stage stochastic optimization, widely used for modeling real-world process optimization …
Provably improved context-based offline meta-rl with attention and contrastive learning
L Li, Y Huang, M Chen, S Luo, D Luo… - arXiv preprint arXiv …, 2021 - arxiv.org
Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with
tremendous potential impact by enabling RL algorithms in many real-world applications. A …
tremendous potential impact by enabling RL algorithms in many real-world applications. A …
Parameterizing non-parametric meta-reinforcement learning tasks via subtask decomposition
Meta-reinforcement learning (meta-RL) techniques have demonstrated remarkable success
in generalizing deep reinforcement learning across a range of tasks. Nevertheless, these …
in generalizing deep reinforcement learning across a range of tasks. Nevertheless, these …
Achieving Fast Environment Adaptation of DRL-Based Computation Offloading in Mobile Edge Computing
One of the key issues in mobile edge computing (MEC) is computation offloading, most
policies of which are developed based on mathematical programming (MP). Due to the high …
policies of which are developed based on mathematical programming (MP). Due to the high …
Pandr: Fast adaptation to new environments from offline experiences via decoupling policy and environment representations
Deep Reinforcement Learning (DRL) has been a promising solution to many complex
decision-making problems. Nevertheless, the notorious weakness in generalization among …
decision-making problems. Nevertheless, the notorious weakness in generalization among …
Evaluations of the gap between supervised and reinforcement lifelong learning on robotic manipulation tasks
Overcoming catastrophic forgetting is of great importance for deep learning and robotics.
Recent lifelong learning research has great advances in supervised learning. However, little …
Recent lifelong learning research has great advances in supervised learning. However, little …