Conservative Critic Estimation via Data Augmentation for Offline Reinforcement Learning

Reinforcement learning is a type of machine learning that involves training algorithms to make decisions based on rewards. However, collecting large amounts of data for reinforcement learning can be time-consuming and expensive. In this paper, we propose an approach to effectively leverage small datasets for reinforcement learning by using offline reinforcement learning initialization, which is trained on the offline dataset, and a generative world model that predicts state transitions. Our approach offers a solution that maximizes the utility of the small offline dataset, successfully training a meaningful initialization that can speed up the online training process.
Our proposed method involves augmenting the training process with generated state transitions to improve the quality of the learned critic. We demonstrate the effectiveness of our approach through experiments on several environments, including Atari games and robotic manipulation tasks. The results show that our method outperforms conventional offline-to-online training methods that use limited datasets without augmentation, achieving better performance in some environments.
To understand our approach, think of the generative world model as a simulator that generates new state transitions based on the patterns it has learned from the offline dataset. By using these generated transitions during the online training process, we can simulate more exploration of the environment, which helps to improve the quality of the learned critic. It’s like having a virtual reality simulation that helps us train the reinforcement learning agent more effectively.
Our approach has several advantages over traditional methods. Firstly, it can handle small datasets, which are common in many real-world applications. Secondly, it does not require additional data collection or manual annotation, making it more practical and cost-effective. Finally, our method can be applied to a wide range of reinforcement learning problems, including both continuous and discrete action spaces.
In summary, our proposed approach leverages small datasets for reinforcement learning by using offline reinforcement learning initialiation and a generative world model to improve the quality of the learned critic. Our method offers a promising solution for improving the practical applicability of reinforcement learning in data-limited scenarios, making it more efficient and cost-effective to train agents that can make good decisions in complex environments.