Challenges in robot reinforcement learning

Applications of reinforcement learning in robotics include:

• Locomotion
• Manipulation
• Autonomous machine control

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 being continuous state-action spaces. The decision is, as per the problem statement, whether to go for DAS algorithms or CAS algorithms. This means at what granular level the robot control should be. One big challenge is the complexity of the real-world systems...