6 accepted papers
Open-world planning poses a significant challenge for general artificial intelligence due to environmental complexity and task diversity, especially in long-term tasks and lifelong learning. Inspired by cognitive theories, we propose M2PA, an open-world multi-memory planning agent. M2PA innovates by…
The challenge of developing agents capable of open-world planning remains fundamental to artificial general intelligence (AGI). While large language models (LLMs) have made progress with their vast world knowledge, their limitations in perception, memory, and reliable reasoning still hinder LLM-base…
Multi-modal large language models (MLLMs) are expanded from large language models (LLMs) with additional capabilities to infer multi-modal data. Current MLLM workflows, when dealing with complex tasks, typically begin by using an LLM to decompose the task into multiple subtasks, then heuristically s…
We explore visual reinforcement learning (RL) using two complementary visual modalities: frame-based RGB camera and event-based Dynamic Vision Sensor (DVS). Existing multi-modality visual RL methods often encounter challenges in effectively extracting task-relevant information from multiple modaliti…
The black-box characteristic of deep reinforcement learning restricts the safe and scalable application of decision models in practical deployment. Existing interpretability methods for deep reinforcement learning models are often inadequate in providing comprehensive insights and generating logical…
Heterogeneous behaviours are widespread in many multi-agent tasks, which have not been paid much attention in the community of multi-agent reinforcement learning. It would be a key factor for improving the learning performance to efficiently characterize and automatically find heterogeneous behaviou…