Robotic Control Using Direct Inverse Reinforcement Learning (No. 0061, 0097)

Summary

Robotic control algorithm based on direct inverse reinforcement learning with density ratio estimation.

The global artificial intelligence market size is projected to reach USD 299 billion by 2026 at a CAGR of 35% over the forecast period. Artificial intelligence is the science and technological progress that is consumed in order to give computer systems the ability to attain problem-solving skills as humans with the aid of learning. Human intelligence is a combination of present consciousness and emotionality while AI attempts to emulate the latter using complex mathematical astute. Understanding behaviors of humans from observation is very crucial for developing artificial systems that can interact with human beings. Since human decision making processes are influenced by rewards/costs associated with selected actions, the problem can be formulated as an estimation of the rewards/costs from observed behaviors. However, one of the critical problems is how to design and prepare an appropriate reward/cost function. The known algorithms are usually very time-consuming even when the model of the environment is available. Here, we present a promising robotic control algorithm developed by a group of researchers led by Prof. Kenji Doya. The developed algorithm overcomes the above problems and provides a more reliable and quick control scheme.

Applications

• Robotic Control
• Analysis of Web Experience – Like Predictions
• Imitation Learning

Advantages

• Model-Free Method – No need to know environmental dynamics
• Data Efficient – Easier to collect data
• Computationally Efficient - Fast