Staged Learning: Boosting Robot Skills with Smart Rewards
![Reward curricula demonstrably enhance the performance of both TD3 and SAC reinforcement learning algorithms across diverse robotic control tasks-DM Control, MobileRobot, and ManiSkill3-with optimal target weights of [latex]w_{\text{target}}=0.5[/latex], [latex]w_{\text{target}}=0.25[/latex], and a range of [latex]w_{\text{target}} \in \{0.25, 0.5, 0.75\}[/latex] respectively, as evidenced by consistently improved base and average rewards measured over the final 50,000 training steps and three random seeds.](https://arxiv.org/html/2603.05113v1/2603.05113v1/fig_ijcai/overview.png)
A new approach to reinforcement learning breaks down complex robotic tasks into manageable stages, improving training efficiency and adaptability.
![Reward curricula demonstrably enhance the performance of both TD3 and SAC reinforcement learning algorithms across diverse robotic control tasks-DM Control, MobileRobot, and ManiSkill3-with optimal target weights of [latex]w_{\text{target}}=0.5[/latex], [latex]w_{\text{target}}=0.25[/latex], and a range of [latex]w_{\text{target}} \in \{0.25, 0.5, 0.75\}[/latex] respectively, as evidenced by consistently improved base and average rewards measured over the final 50,000 training steps and three random seeds.](https://arxiv.org/html/2603.05113v1/2603.05113v1/fig_ijcai/overview.png)
A new approach to reinforcement learning breaks down complex robotic tasks into manageable stages, improving training efficiency and adaptability.

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A new database is bridging the gap between theoretical predictions and experimental results, accelerating the discovery of next-generation two-dimensional materials.