Activities per year
Reinforcement learning is a popular machine learning paradigm which can find near optimal solutions to complex problems. Most often, these procedures involve function approximation using neural networks with gradient based updates to optimise weights for the problem being considered. While this common approach generally works well, there are other update mechanisms which are largely unexplored in reinforcement learning. One such mechanism is Extreme Learning Machines. These were initially proposed to drastically improve the training speed of neural networks and have since seen many applications. Here we attempt to apply extreme learning machines to a reinforcement learning problem in the same manner as gradient based updates. This new algorithm is called Extreme Q-Learning Machine (EQLM). We compare its performance to a typical Q-Network on the cart-pole task - a benchmark reinforcement learning problem - and show EQLM has similar long-term learning performance to a Q-Network.
|Number of pages||7|
|Publication status||Published - 24 Jul 2020|
|Event||IEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom|
Duration: 19 Jul 2020 → 24 Jul 2020
|Conference||IEEE World Congress on Computational Intelligence 2020|
|Period||19/07/20 → 24/07/20|
- reinforcement learning
- extreme learning machine (ELM)
- neural network
Wilson, C., Riccardi, A., & Minisci, E. (2020). A novel update mechanism for Q-Networks based on extreme learning machines. Paper presented at IEEE World Congress on Computational Intelligence 2020, Glasgow, United Kingdom.