Is human-in-the-loop reinforcement learning enhanced if the robot emotes its learning progress?: An experimental study

As technology continues to evolve at a rapid pace, robots are becoming an increasingly common sight in our daily lives. <br/>Robots that work with humans need to adapt to a variety of users and tasks, and learn to optimise their behaviour. For non-specialist users to interact with such robots, the robot's learning process needs to be transparent...

Eligibility traces and forgetting factor in recursive least-squares-based temporal difference

We propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal Difference (RLS-TD). Instead of using the standard mechanism of eligibility traces (resulting in RLS-TD((Formula presented.))), we propose to use the forgetting factor commonly used in gradient-based or least-square estimation, and we show that...

Community energy storage operation via reinforcement learning with eligibility traces

The operation of a community energy storage system (CESS) is challenging due to the volatility of photovoltaic distributed generation, electricity consumption, and energy prices. Selecting the optimal CESS setpoints during the day is a sequential decision problem under uncertainty, which can be solved using dynamic learning methods. This...

Reinforcement Learning on autonomous humanoid robots

Service robots have the potential to be of great value in households, health care and other labor intensive environments. However, these environments are typically unique, not very structured and frequently changing, which makes it difficult to make service robots robust and versatile through manual programming. Having robots learn to solve...