RangL: A Reinforcement Learning Competition Platform

no code implementations • 28 Jul 2022 • Viktor Zobernig, Richard A. Saldanha, Jinke He, Erica van der Sar, Jasper van Doorn, Jia-Chen Hua, Lachlan R. Mason, Aleksander Czechowski, Drago Indjic, Tomasz Kosmala, Alessandro Zocca, Sandjai Bhulai, Jorge Montalvo Arvizu, Claude Klöckl, John Moriarty

The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems.

OpenAI Gym reinforcement-learning +1

Improvements to Modern Portfolio Theory based models applied to electricity systems

1 code implementation • 17 May 2021 • Gabriel Malta Castro, Claude Klöckl, Peter Regner, Johannes Schmidt, Amaro Olimpio Pereira Jr

More specifically, we address generation costs, system demand, and firm energy output, present a formal model and apply it to the case of Brazil.

Computational Performance of Deep Reinforcement Learning to find Nash Equilibria

1 code implementation • 26 Apr 2021 • Christoph Graf, Viktor Zobernig, Johannes Schmidt, Claude Klöckl

We test the performance of deep deterministic policy gradient (DDPG), a deep reinforcement learning algorithm, able to handle continuous state and action spaces, to learn Nash equilibria in a setting where firms compete in prices.

reinforcement-learning Reinforcement Learning (RL)