Authors: Vineet Jain, Siamak Ravanbakhsh

Published on: October 04, 2023

Impact Score: 8.22

Arxiv code: Arxiv:2310.02505

Summary

• What is new: Merlin introduces a novel method of incorporating denoising diffusion models into goal-conditioned reinforcement learning, performing diffusion in the state space.
• Why this is important: Existing goal-conditioned reinforcement learning approaches are inefficient and less effective in reaching specified goals from arbitrary initial states.
• What the research proposes: Merlin constructs trajectories moving away from goal states and learns a goal-conditioned policy to reverse these deviations without a separate value function.
• Results: Demonstrated performance improvements in offline goal-reaching tasks and computational efficiency over other diffusion-based reinforcement learning methods.

Technical Details

Technological frameworks used: Denoising diffusion models, Reinforcement learning

Models used: Goal-conditioned policy

Data used: Offline goal-reaching tasks

Potential Impact

Gaming, robotics, autonomous vehicles, AI-driven simulation companies

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