In this paper we target the problem of transferring policies across multiple environments with different dynamics parameters and motor noise variations, by introducing a framework that decouples the processes of policy learning and system identification. Efficiently transferring learned policies to an unknown environment with changes in dynamics configurations in the presence of motor noise is very important for operating robots in the real world, and our work is a novel attempt in that direction. We introduce MANGA: Method Agnostic Neural-policy Generalization and Adaptation, that trains dynamics conditioned policies and efficiently learns to estimate the dynamics parameters of the environment given off-policy state-transition rollouts in the environment. Our scheme is agnostic to the type of training method used - both reinforcement learning (RL) and imitation learning (IL) strategies can be used. We demonstrate the effectiveness of our approach by experimenting with four different MuJoCo agents and comparing against previously proposed transfer baselines.

该论文介绍了一种名为MANGA的神经策略泛化和适应方法，通过分离策略学习和系统识别的过程，将学习到的策略有效地转移到具有不同动态参数和电机噪声变化的未知环境中，我们通过4个不同的MuJoCo代理实验来证明了该方法的有效性。

MANGA: 方法无关的神经策略泛化与适应