Instructed Diffuser With Temporal Condition Guidance for Offline Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Diffusion models show promise in computer vision and NLP.
• Their application in reinforcement learning (RL) is emerging, treating decision-making as sequential generation.
• Effectively incorporating temporal information to guide diffusion models remains a challenge.

Purpose of the Study:

• To investigate controllable generation using refined temporal conditions.
• To analyze the importance and comparison of different temporal conditions in sequential generation.
• To propose a novel temporally-conditional diffusion model for enhanced RL.

Main Methods:

• Developed the Temporally-Composable Diffuser (TCD), a diffusion model that extracts and utilizes temporal information from interaction sequences.
• Separated sequences into historical, immediate, and prospective temporal conditions, each preserving non-overlapping information.
• Employed joint usage of these conditions to guide the diffusion process for controllable generation.

Main Results:

• Demonstrated the importance of temporal conditions in various sequential generation scenarios.
• TCD achieved state-of-the-art (SOTA) or comparable performance in offline reinforcement learning tasks.
• Extensive experiments validated the model's applicability and effectiveness.

Conclusions:

• Temporally-Composable Diffuser (TCD) offers an effective approach for controllable generation in RL by leveraging refined temporal information.
• The proposed method of separating sequences into distinct temporal conditions enhances generation control.
• TCD shows significant potential for advancing sequential decision-making in offline RL settings.