Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning

Wenzhen Huang, Qiyue Yin, Junge Zhang +1

Model-based reinforcement learning (RL) is more sample efficient than model-free RL by using imaginary trajectories generated by the learned dynamics model. When the model is inacc…