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Related Questions
- What are the key differences between on-policy and off-policy reinforcement learning in adapting models to new tasks or domains?
- How does the use of on-policy and off-policy approaches impact the learning efficiency and stability in adapting models to new tasks or domains?
- Can you explain the role of exploration-exploitation trade-offs in on-policy and off-policy learning when adapting models to new tasks or domains?
- What are some common challenges faced by on-policy and off-policy methods when adapting models to new tasks or domains, and how can they be addressed?
- How do on-policy and off-policy methods handle multi-task learning and transfer learning in adapting models to new tasks or domains?
- What are the differences in the choice of exploration strategies between on-policy and off-policy methods when adapting models to new tasks or domains?
- Can you discuss the implications of using on-policy and off-policy methods on the sample efficiency and generalizability of models in adapting to new tasks or domains?
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