Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control
Murad Dawood,Nils Dengler,Jorge de Heuvel,Maren Bennewitz,Murad Dawood,Nils Dengler,Jorge de Heuvel,Maren Bennewitz
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of the reward function requires detailed domain expertise and tedious fine-tuning to ensure that agents are able to learn the desired behaviour. Using a sparse reward conveniently mitigates these challenges. However, the sparse reward represents a challenge on its own, often resulting in unsuccessful ...
