Offline reinforcement learning (offline RL) [30,34]refers tothe setting where policies are trained using static, previously collected datasets. This presents an attractive paradigm for data reuse and safe policy learning in many applications, such as healthcare [62], autonomous driving [65], robotics [25, 48], and personalized recommendation systems [59].

On both synthetic and real data, we empirically show that our proposed algorithm is able to learn better CVaRoptimizedpolicies.

We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels.

However, the automation of MIS and RAMIS through the use of AI has been slow due to difficulties in data acquisition and curation, partially caused by the ethical considerations of training, testing and deploying AI models in medical environments.

Changes inimage statistics ordistracting background elements are pitfalls that prevent generalization and real-world applicability of such control policies.

Contrastingly, others have observed that larger batch sizes tend to converge to "sharper" optimization

Since the policy used to collect transitions is changing throughout learning, the replay memory contains data coming from a mixture of policies (that differ from the agent's current policy), and