Abstract
Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest.
Original language | English (US) |
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State | Published - 2023 |
Externally published | Yes |
Event | 11th International Conference on Learning Representations, ICLR 2023 - Kigali, Rwanda Duration: May 1 2023 → May 5 2023 |
Conference
Conference | 11th International Conference on Learning Representations, ICLR 2023 |
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Country/Territory | Rwanda |
City | Kigali |
Period | 5/1/23 → 5/5/23 |
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language