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Humans have the ability to reuse previously learned policies to solve newtasks quickly, and reinforcement learning (RL) agents can do the same bytransferring knowledge from source policies to a related target task. TransferRL methods can reshape the policy optimization objective (optimizationtransfer) or influence the behavior policy (behavior transfer) using sourcepolicies. However, selecting the appropriate source policy with limited samplesto guide target policy learning has been a challenge. Previous methodsintroduce additional components, such as hierarchical policies or estimationsof source policies' value functions, which can lead to non-stationary policyoptimization or heavy sampling costs, diminishing transfer effectiveness. Toaddress this challenge, we propose a novel transfer RL method that selects thesource policy without training extra components. Our method utilizes the Qfunction in the actor-critic framework to guide policy selection, choosing thesource policy with the largest one-step improvement over the current targetpolicy. We integrate optimization transfer and behavior transfer (IOB) byregularizing the learned policy to mimic the guidance policy and combining themas the behavior policy. This integration significantly enhances transfereffectiveness, surpasses state-of-the-art transfer RL baselines in benchmarktasks, and improves final performance and knowledge transferability incontinual learning scenarios. Additionally, we show that our optimizationtransfer technique is guaranteed to improve target policy learning.

IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse