RL is about learning expected-reward-maximizing policies for environments that you get to interact with. Common benchmarks currently mostly include games (e.g. ATARI, AlphaGo, VizDoom), physics-based animation (e.g. https://www.cs.ubc.ca/~van/papers/2016-TOG-deepRL/index.html), or (simulated) robotics-like tasks (e.g. MuJoCo). But the core algorithms (such as policy gradients) can be used more generally in settings that don't necessarily look like environments as usual, but where you want to train a network with stochastic nodes, such as in hard attention, etc.
RL is a funny area; A lot of AI researchers get excited about it (mostly motivated by its promise as the formalism that leads to AGI), and yet despite the hype it has so far had very little impact in the industry so far (the Google data center application possibly being an exception, though it was more "RL" than RL, with quotes). It has some promise for Robotics in the real world, but not applied directly and naively. The way that will play out is likely through behavior cloning on human demonstrations or on outputs of trajectory optimizers from simulation, or possibly RL fine-tuning in simulation transferred to real world. But it's still quite early to tell.
On this topic, fun story, the most impressive robots I'm aware of right now are from Boston Dynamics and as they mentioned at this year's NIPS they use ZERO machine learning. Forget deep learning or even deep reinforcement learning. Zero Machine Learning.
I gave a talk last week about some of our RL experiments @ OpenAI and someone came to me after the talk, described their (straight forward) supervised learning problem and asked me how they can apply RL to it. This, to me, is an alarming sign of damaging hype to the community. You don't use RL for your SL problems. You can if you really want to (e.g. reward = 1.0 if you guess the correct label or -1.0 otherwise), but you really don't want to. You're lucky, use your labels, business as usual.