论文标题
通过概率动力学模型预测SIM到真实传输
Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models
论文作者
论文摘要
我们提出了一种预测RL策略的SIM到现实传输性能的方法。我们的转移度量简化了训练设置（例如算法，超参数，随机化）和模拟中的策略，而无需进行大量且耗时的现实世界推出。概率动力学模型与策略一起训练，并在一组固定的现实轨迹上进行评估以获得转移度量。实验表明，转移度量与模拟和实际机器人环境中的策略性能高度相关，以进行复杂的操纵任务。我们进一步表明，转移指标可以预测培训设置对政策转移绩效的影响。
We propose a method to predict the sim-to-real transfer performance of RL policies. Our transfer metric simplifies the selection of training setups (such as algorithm, hyperparameters, randomizations) and policies in simulation, without the need for extensive and time-consuming real-world rollouts. A probabilistic dynamics model is trained alongside the policy and evaluated on a fixed set of real-world trajectories to obtain the transfer metric. Experiments show that the transfer metric is highly correlated with policy performance in both simulated and real-world robotic environments for complex manipulation tasks. We further show that the transfer metric can predict the effect of training setups on policy transfer performance.
