Mar 5, 2020 · In order to solve above challenges, in this study, we proposed a simple but powerful reward shaping method, namely Dense2Sparse. It combines the ...

The testing results show that the proposed Dense2Sparse method is capable of getting a higher expected reward and success rate compared with the ones using ...

The experiment results show that the Dense2Sparse method obtained higher expected reward compared with the ones using standalone dense reward or sparse reward, ...

Jul 11, 2022 · We evaluated our Dense2Sparse method with a series of ablation experiments using the deep learning-based state estimator with system uncertainty ...

In this study, we propose the reward shaping method,. Dense2Sparse, in the DRL training process to balance the effectiveness and efficiency of the learning for ...

Mar 5, 2020 · In this study, we proposed a simple but effective reward shaping strategy, Dense2Sparse to balance the efficiency and effectiveness of the DRL ...

2023. Balance between efficient and effective learning: Dense2Sparse reward shaping for robot manipulation with environment uncertainty. K Dong, Y Luo, E ...

The experiment results show that the Dense2Sparse method obtained higher expected reward compared with the ones using standalone dense reward or sparse reward, ...

Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty. AIM 2022: 1192-1198. [i2].

Aug 1, 2022 · Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty. View Code