5 accepted papers
Offline multi-agent reinforcement learning (MARL) with pre-training paradigm, which uses a large quantity of trajectories for offline pre-training and online deployment, has become fashionable lately. While performing well on various tasks, conventional pre-trained decision-making models based on im…
In unsupervised reinforcement learning, agents traverse a reward-free environment, aiming for rapid generalisation to subsequent tasks. This strategy offers a compelling resolution to the challenges of sample efficiency. Nevertheless, environments are frequently saturated with excessive information.…
Enhancing automatic speech recognition (ASR) performance by leveraging additional multimodal information has shown promising results in previous studies. However, most of these works have primarily focused on utilizing visual cues derived from human lip motions. In fact, context-dependent visual and…
In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking a weighted combination of two different speech features (e.g., mel-spectrograms or MFCC) as the input, and recognizing b…
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as th…