BATE
A bottleneck-adaptive targeted exploration method within the AtomicRL framework that dynamically allocates sampling weights and exploration incentives according to the degree of mastery of atomic subtasks, realizing data-driven adaptive curriculum learning.
Timeline (last 90 days)
Bottleneck-Adaptive Target Exploration (BATE) dynamically coordinates task sampling and local exploration incentives according to each atomic subtask's mastery level; poorly mastered subtasks receive more sampling opportunities and exploration rewards.
BATE implements data-driven adaptive curriculum learning: it dynamically allocates sampling probabilities and local exploration incentives according to each atomic subtask's current mastery level.
AtomicRL's average 21.3% real-world improvement depends on the effectiveness of the Real2Sim2Real transfer pipeline, whereas the improvements on simulation benchmarks mainly come from SAPO and BATE's improvements to credit assignment and exploration, so the two have different sources of contribution.
All Facts (3)
Bottleneck-Adaptive Target Exploration (BATE) dynamically coordinates task sampling and local exploration incentives according to each atomic subtask's mastery level; poorly mastered subtasks receive more sampling opportunities and exploration rewards.
50%UnverifiedBATE implements data-driven adaptive curriculum learning: it dynamically allocates sampling probabilities and local exploration incentives according to each atomic subtask's current mastery level.
50%UnverifiedAtomicRL's average 21.3% real-world improvement depends on the effectiveness of the Real2Sim2Real transfer pipeline, whereas the improvements on simulation benchmarks mainly come from SAPO and BATE's improvements to credit assignment and exploration, so the two have different sources of contribution.
50%