Reinforcement learning (RL) faces challenges in sample collection efficiency and real-world adoption due to standard methods struggling in risky exploration environments.
Practical Solutions with Policy-Guided Diffusion (PGD):
PGD addresses these challenges by modeling entire trajectories, training a diffusion model on pre-collected data to generate synthetic trajectories under the behavior policy, aligning with the target policy to reduce divergence.
Key Benefits of PGD:
– Effectiveness: Agents trained with synthetic experience from PGD outperform those trained on unguided synthetic data or directly on the offline dataset.
– Guidance Coefficient Tuning: Tuning the guidance coefficient enables sampling of trajectories with high action likelihood across a range of target policies.
– Low Dynamics Error: PGD achieves significantly lower error across all target policies compared to other methods.
– Training Stability: PGD demonstrates consistent performance improvement and stability compared to other training approaches.
Value and Practical Implementation:
PGD offers a controllable method for synthetic trajectory generation in offline RL, achieving competitive performance and improving downstream agent performance across diverse environments and behavior policies.
Embracing AI for Business Transformation:
Consider how PGD and other AI solutions can redefine your work and automate customer engagement to stay competitive. Define KPIs, select suitable AI solutions, and implement gradually for measurable impacts on business outcomes.
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