New SMAC method enables robust offline-to-online reinforcement learning transfer

Researchers have developed a new method called Score-Matched Actor-Critic (SMAC) to improve the transfer of reinforcement learning models from offline to online environments. Traditional methods often see performance drops when fine-tuning offline-trained models online, a problem attributed to performance valleys in the loss landscape. SMAC addresses this by regularizing the Q-function during offline training, ensuring a smoother transition to online learning. Experiments show SMAC achieves significant regret reduction and maintains performance across various D4RL tasks when transferring to algorithms like Soft Actor-Critic and TD3. AI

IMPACT Enables more stable and effective deployment of reinforcement learning agents in real-world scenarios by improving offline-to-online transfer.

RANK_REASON The cluster contains an academic paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• D4RL
• Nathan S. De Lara
• reinforcement learning
• Score Matched Actor-Critic
• Soft Actor--Critic
• TD3

• paper
• other

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