Multimodal Reinforcement Learning Research Articles

Prediction of Bitcoin Prices Based on Blockchain Information: A Deep Reinforcement Learning Approach

Bitcoin, the first decentralized cryptocurrency, has attracted significant attention from investors and researchers alike due to its volatile and unpredictable price movements. However, predicting the price of Bitcoin remains a challenging task. This paper presents a detailed literature review on previous studies that have attempted to predict the price of Bitcoin. It discusses the main drivers of Bitcoin prices, including its attractiveness, macroeconomic and financial factors with a particular focus on the use of Blockchain information. We apply time series to daily data for the period from 28/04/2013 to 28/01/2023. We used Python and TensorFlow library version 2.11.0 and propose a deep multimodal reinforcement learning policy combining Convolutional Neural Network (CNN) and Long ShortTerm Memory (LSTM) neural network for cryptocurrencies’ prices prediction. Also, this study attempts to predict the price of Bitcoin using a special type of deep neural networks, a Deep Autoencoders. Two results are worth noting: Autoencoders turns out to be the best method of predicting Bitcoin prices, and Bitcoin-specific Blockchain information is the most important variable in predicting Bitcoin prices. This study highlights the potential utility of incorporating Blockchain factors in price prediction models. Also, our findings show that sentiment indicator, Ethereum, XRP and Doge Coin prices, global currency ratio, macroeconomic factors, and Blockchain information of Ethereum did not contribute significantly toward predicting Bitcoin prices. These conclusions provide decision support for investors and a reference for the governments to design better regulatory policies.

Multimodal Reinforcement Learning for Embedding Networks and Medication Recommendation in Parkinson’s Disease

Multimodal Reinforcement Learning for Embedding Networks and Medication Recommendation in Parkinson’s Disease

Multi-dimensional fusion: transformer and GANs-based multimodal audiovisual perception robot for musical performance art.

In the context of evolving societal preferences for deeper emotional connections in art, this paper explores the emergence of multimodal robot music performance art. It investigates the fusion of music and motion in robot performances to enhance expressiveness and emotional impact. The study employs Transformer models to combine audio and video signals, enabling robots to better understand music's rhythm, melody, and emotional content. Generative Adversarial Networks (GANs) are utilized to create lifelike visual performances synchronized with music, bridging auditory and visual perception. Multimodal reinforcement learning is employed to achieve harmonious alignment between sound and motion. The study leverages Transformer models to process audio and video signals in robot performances. Generative Adversarial Networks are employed to generate visually appealing performances that align with the musical input. Multimodal reinforcement learning is used to synchronize robot actions with music. Diverse music styles and emotions are considered in the experiments. Performance evaluation metrics include accuracy, recall rate, and F1 score. The proposed approach yields promising results across various music styles and emotional contexts. Performance smoothness scores exceed 94 points, demonstrating the fluidity of robot actions. An accuracy rate of 95% highlights the precision of the system in aligning robot actions with music. Notably, there is a substantial 33% enhancement in performance recall rate compared to baseline modules. The collective improvement in F1 score emphasizes the advantages of the proposed approach in the realm of robot music performance art. The study's findings demonstrate the potential of multimodal robot music performance art in achieving heightened emotional impact. By combining audio and visual cues, robots can better interpret and respond to music, resulting in smoother and more precise performances. The substantial improvement in recall rate suggests that the proposed approach enhances the robots' ability to accurately mirror the emotional nuances of the music. These results signify the potential of this approach to transform the landscape of artistic expression through robotics, opening new avenues for emotionally resonant performances.

A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning.

Despite the emergence of various human-robot collaboration frameworks, most are not sufficiently flexible to adapt to users with different habits. In this article, a Multimodal Reinforcement Learning Human-Robot Collaboration (MRLC) framework is proposed. It integrates reinforcement learning into human-robot collaboration and continuously adapts to the user's habits in the process of collaboration with the user to achieve the effect of human-robot cointegration. With the user's multimodal features as states, the MRLC framework collects the user's speech through natural language processing and employs it to determine the reward of the actions made by the robot. Our experiments demonstrate that the MRLC framework can adapt to the user's habits after repeated learning and better understand the user's intention compared to traditional solutions.

Multifingered Grasping Based on Multimodal Reinforcement Learning

In this work, we tackle the challenging problem of grasping novel objects using a high-DoF anthropomorphic hand-arm system. Combining fingertip tactile sensing, joint torques and proprioception, a multimodal agent is trained in simulation to learn the finger motions and to determine when to lift an object. Binary contact information and level-based joint torques simplify transferring the learned model to the real robot. To reduce the exploration space, we first generate postural synergies by collecting a dataset covering various grasp types and using principal component analysis. Curriculum learning is further applied to adjust and randomize the initial object pose based on the training performance. Simulation and real robot experiments with dedicated initial grasping poses show that our method outperforms two baseline models in the grasp success rate both for seen and unseen objects. This learning approach further serves as a fundamental technology for complex in-hand manipulations based on multi-sensory the system.

MMDP: A Mobile-IoT Based Multi-Modal Reinforcement Learning Service Framework

With the development of GPS technology, a new Mobile Internet of Things (M-IoT) is emerging, which perceives the city's rhythm and pulse day and night to collect a large scale of city data. It is urgent to innovate M-IoT service system for these large-scale and heterogeneous data. To cope with the problem, this article proposes a Mobile-IoT based multi-modal reinforcement learning service framework from data perspective, which has three highlights, i) Developing Action-aware High-order Transition Tensor ( $AHTT$ A H T T ) to fuse the heterogeneous data from M-IoTs in a unified form. ii) Developing Multi-modal Markov Decision Process ( $MMDP$ M M D P ) to model the multi-modal reinforcement learning for M-IoT service framework. iii) Developing Tensor Policy Iteration algorithm ( $TPIA$ T P I A ) to solve the optimal tensor policy. Due to using tensor keeps the multi-modal relations of the context information in the process of solving the optimal policy. The proposed M-IoT service system provides more personalized service for taxi drivers. The experiment results shows that most taxi drivers earn more revenue according to the tensor policy.

Score Following as a Multi-Modal Reinforcement Learning Problem

Score following is the process of tracking a musical performance (audio) in a corresponding symbolic representation (score). While methods using computer-readable score representations as input are able to achieve reliable tracking results, there is little research on score following based on raw score images. In this paper, we build on previous work that formulates the score following task as a multi-modal Markov Decision Process (MDP). Given this formal definition, one can address the problem of score following with state-of-the-art deep reinforcement learning (RL) algorithms. In particular, we design end-to-end multi-modal RL agents that simultaneously learn to listen to music recordings, read the scores from images of sheet music, and follow the music along in the sheet. Using algorithms such as synchronous Advantage Actor Critic (A2C) and Proximal Policy Optimization (PPO), we reproduce and further improve existing results. We also present first experiments indicating that this approach can be extended to track real piano recordings of human performances. These audio recordings are made openly available to the research community, along with precise note-level alignment ground truth.