Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
Many goal-reaching reinforcement learning (RL) tasks have empirically verified that rewarding the agent on subgoals improves convergence speed and practical performance. We attempt to provide a theoretical framework to quantify the computational benefits of rewarding the completion of subgoals, in t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AI Access Foundation
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |