MANGA: Method Agnostic Neural-policy Generalization and Adaptation
This work addresses the challenge of operating robots in real-world settings with unknown dynamics, representing a novel attempt in policy transfer.
The paper tackles the problem of transferring learned policies across environments with varying dynamics and motor noise by introducing MANGA, a framework that decouples policy learning and system identification, and demonstrates its effectiveness on four MuJoCo agents with comparisons to baselines.
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.