I'm a newbie to reinforcement learning. While studying reinforcement learning, a question arose about how to apply reinforcement learning in the real world.
Assuming that a reinforcement learning agent trained in simulation is used in the real world, do they usually only use the optimal policy learned in the simulator? Or are they need additional exploration (learning) in the real world?
In my view, the latter approach is quite impractical in real-world environments where failure by agents' actions is fatal. However, it also seems very difficult for the simulation environment to reflect the real world.
What approach is being adopted by the industry field?