The disassembly complexity of end-of-life products increases continuously. Traditional
methods are facing difficulties in solving the decision-making and control problems of …

In this work we identify the dormant neuron phenomenon in deep reinforcement learning,
where an agent's network suffers from an increasing number of inactive neurons, thereby …

Soft Actor-Critic is a state-of-the-art reinforcement learning algorithm for continuous action
settings that is not applicable to discrete action settings. Many important settings involve …

The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

Data efficiency is a key challenge for deep reinforcement learning. We address this problem
by using unlabeled data to pretrain an encoder which is then finetuned on a small amount of …

To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …

Abstract This paper introduces Elastic Decision Transformer (EDT), a significant
advancement over the existing Decision Transformer (DT) and its variants. Although DT …

Many reinforcement learning (RL) tasks provide the agent with high-dimensional
observations that can be simplified into low-dimensional continuous states. To formalize this …

Planning methods can solve temporally extended sequential decision making problems by
composing simple behaviors. However, planning requires suitable abstractions for the states …

Model efficiency is a critical aspect of developing and deploying machine learning models.
Inference time and latency directly affect the user experience, and some applications have …