In analyzing the improvement in the Humanoid task, the iterations showed that adjusting reward weights progressively enhanced performance, resulting in a remarkable increase in RTS to 8.125.
The application of different penalty terms and reward weightings over successive iterations of the ICPL led to a significant enhancement in humanoid performance, showcasing the importance of fine-tuning rewards.
Initial rewards calculated for the humanoid task and the adjustments made to the weightings reflect a systematic approach to optimizing performance through a structured trial process.
The results indicate that increasing the weight on the speed reward consistently improved the RTS, demonstrating the impact of strategic reward balancing on agent performance.
#machine-learning #reward-optimization #humanoid-robotics #performance-metrics #iterative-improvement
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