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The document presents a novel algorithm called Scheduled Multi-Task Training (SMT) aimed at improving multi-task reinforcement learning (RL) by prioritizing more challenging tasks to enhance learning efficiency. SMT employs a dynamic task prioritization strategy and a reset mechanism to mitigate the simplicity bias, which often leads to negative transfer in learning. Empirical results demonstrate that SMT significantly outperforms baseline algorithms on the Meta-World benchmark, showcasing its effectiveness
- Author
- yujiachenhit
- Language
- EN