Yes, of course reinforcement learning is intrinsically limited. The current state of the art is deep learning, but even this technique is still statistical machine learning. In other words, it works by devising a cost or objective function (in the case of go, winning the game, which is clearly defined), and optimises it by starting from examples. In the case of the game of go, the hundreds of thousands of games already annotated and played by humans. Reinforcement learning artificially creates many new games from old ones, and playing these, which helps finding a better local minimum in the cost function.
Now "strong" AI or whatever you want to call some version of human-like AI have to cope with situations where there is no clearly defined cost function, and very few if any example to start from. The complexity of these problems is way beyond that of Go, which is a very limited system. Note that these problems are every day ones that humans have to cope with every day, like how do I persuade my clients to pay me ? how do I explain to my work partners the value of my work ? they typically include other complex systems that are not rule-based or for which the rules may exist but are unknown. Example: we do not know enough of physics to accurately predict next week's weather. How do we improve the situation?
You will not be able to better predict next week's weather with reinforcement learning.