Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms
This work addresses the challenge of efficient training for robotic control tasks, but it is incremental as it builds on established methods without introducing a new paradigm.
The paper tackled the problem of training robotic arms for continuous control using deep reinforcement learning, and found that a novel combination of existing methods with preprocessing techniques reduced training time and improved convergence in a simulated environment.
Deep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state spaces. With deep reinforcement learning being an active area of research and many concurrent inventions, we decided to focus on a relatively simple robotic task to evaluate a set of ideas that might help to solve recent reinforcement learning problems. We test a newly created combination of two commonly used reinforcement learning methods, whether it is able to learn more effectively than a baseline. We also compare different ideas to preprocess information before it is fed to the reinforcement learning algorithm. The goal of this strategy is to reduce training time and eventually help the algorithm to converge. The concluding evaluation proves the general applicability of the described concepts by testing them using a simulated environment. These concepts might be reused for future experiments.