Improving Success Rate in Robotics Task Completion Using Model-Based Reinforcement Learning
Open Access Deposited(ii) a multimodal fusion, MambaBind, which employs Mamba state-space models and a contrastive alignment objective to better combined multiple sensor inputs
computational efficiency, multimodal sensor fusion, and multi-agent scalability. Reliance on traditional recurrent neural networks can bottleneck learning in long horizon/sparse reward environments, simple concatena- tion of multiple sensor inputs underutilizes cross-modal structure, and the limited availability of standardized model-based multi-agent reinforcement learning algorithms particularly public multi- agent variants of DreamerV3 further complicates adoption. This praxis develops and evaluates a hybrid MBRL framework built upon DreamerV3 with three core innovations
(i) integration of the Light Recurrent Unit and the Stack Recurrent Cell to improve computational efficiency and long-term dependency modeling
and (iii) a scalable multi-agent design that uses variational autoencoders to enable a shared world model to generalize across scenarios with varying numbers of agents. The framework is evalu- ated on Atari-100k, Crafter, CARLA, and SMACv2. Results show that the recurrent variants are more performant than both baseline on long-horizon/sparse reward tasks. MambaBind improves performance over concatenation in CARLA. The multi-agent framework scales and generalizes, outperforming established model-based and model-free multi-agent baselines. Overall, this work provides a hybrid scalable framework that improves sample efficiency, accelerates training, and enables more capable general-purpose agents for complex robotics applications.
Model-based reinforcement learning is promising for robotics, yet leading approaches suchas DreamerV3 face three problems that hinder real-world deployment
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