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Found 7 papers, 4 papers with code
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement Learning
Despite the impressive performance across numerous tasks, large language models (LLMs) often fail in solving simple decision-making tasks due to the misalignment of the knowledge in LLMs with environments.
MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones
Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size.
On Optimizing Interventions in Shared Autonomy
Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance.
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks
The present work focuses on using SNNs in combination with deep reinforcement learning in ATARI games, which involves additional complexity as compared to image classification.
Asynchronous Multi-Agent Actor-Critic with Macro-Actions
Many realistic multi-agent problems naturally require agents to be capable of performing asynchronously without waiting for other agents to terminate (e. g., multi-robot domains).
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination reliably.
