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Training a single policy typically relies on large-scale parallel simulation and curriculum learning in reinforcement learning, gradually increasing terrain difficulty.
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10/26/2026
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UnverifiedThe cost of training a single policy is a significant rise in training difficulty, because the optimal exploration paths for different skills may conflict with one another.80% similarUnverifiedCurriculum learning guides models to progressively build generalizable capabilities by arranging the difficulty and diversity of training tasks.79% similarPartially VerifiedCurriculum learning is an effective means of improving the training success rate: start with simple scenarios and gradually increase the difficulty, avoiding the situation where the agent cannot obtain an effective reward signal in the early stage because the task is too difficult.76% similarUnverifiedBehavior cloning pretraining solves the cold-start problem in reinforcement learning: pure reinforcement learning in sparse-reward environments often requires hundreds of millions of interactions before learning a minimally usable policy.76% similarUnverifiedHierarchical reinforcement learning splits the policy into multiple layers, with the upper layer outputting abstract intents or subgoals and the lower layer handling concrete execution, used to alleviate the problems of an overly large action space and sparse exploration in long-horizon tasks, but each layer needs its own credit assignment signal, making training less stable than a single-layer policy.76% similar
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