arXiv:2605.27834v1 Announce Type: new Abstract: We study the transfer of rewards learned using inverse reinforcement learning from expert demonstrations in one environment to reinforcement learning in a new, different environment. This arises naturally when demonstrations are collected in a controlled environment. We formulate the problem as a joint system of Bellman equations across the source and target environments and develop minimax estimators for the target soft-$q$-function. Whereas a sequential solution approach first estimates the source reward and then plugs it into the target contro
This research addresses a practical bottleneck in applying Inverse Reinforcement Learning (IRL) to real-world scenarios, particularly where demonstration environments differ from deployment environments.
Improving reward transfer for IRL could accelerate the development and deployment of autonomous AI systems, reducing reliance on costly and time-consuming manual reward engineering in new contexts.
The ability to more effectively transfer learned rewards means AI agents can adapt faster to diverse environments, potentially lowering the barrier for AI adoption in varied applications.
- · AI developers
- · Robotics industry
- · Simulation platforms
- · Logistics and automation
- · Companies relying on extensive manual AI environment setup
This research directly advances the capability for AI agents to learn from demonstrations and generalize those learnings.
Improved generalization could lead to more robust and adaptable AI systems in complex, real-world operational environments.
The reduced cost and increased efficiency of deploying adaptable AI agents could accelerate automation across various sectors, creating new economic efficiencies.
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Read at arXiv cs.LG