Title: Reinforcement Learning for Bandits with Continuous Actions and Large Context Spaces. Abstract: We consider the challenging scenario of contextual bandits with continuous actions and large input ``context'' spaces, e.g. images. We posit that by modifying reinforcement learning (RL) algorithms for continuous control, we can outperform hand-crafted contextual bandit algorithms for continuous actions on standard benchmark datasets, i.e. vector contexts. We demonstrate that parametric policy networks outperform recently published tree-based policies in both average regret and costs on held-out samples. Furthermore, in contrast to previous work, we successfully demonstrate that RL algorithms can generalise contextual bandit problems with continuous actions to large context spaces. We obtain state-of-the-art performance using RL and significantly outperform previous methods on image contexts. Lastly, we introduce a new contextual bandits domain with multi-dimensional continuous action space and image context. 