April 2018
Intermediate to advanced
334 pages
10h 18m
English
Applications of reinforcement learning in robotics include:
As discussed previously, in order for a reinforcement learning agent to perform better in a real-world task it should have a well-defined, domain-specific reward function, which is hard to implement. This problem is being tackled by using techniques such as apprenticeship learning. Another approach to solve the uncertainty in reward is to continuously update the reward functions as per the state so that the most optimized policy is generated. This approach is called inverse reinforcement learning.
Robot reinforcement learning is a hard problem to solve owing to many challenges. The first ...