Why you should love RL?

Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior — how to map situations to actions — in an environment to maximize the total reward. The algorithm is not told which actions to take, but instead must discover which actions yield the most immediate and subsequent reward by trying them and observing how the environment responds. This discovery process is quite similar to children exploring the world around them and learning actions that help them achieve a goal.

As it has a capacity to seek new and creative ways in unseen dynamics without the help of a supervisor, RL is an unique tool that has the potential to transform our world, especially to mitigate climate change. For instance, when we look at 36 solutions to halve emissions by 2030 in the Exponential Roadmap by J. Falk, what’s fascinating is that a proper optimization and control system powered by RL can be used for each of those items.

Source : Exponential roadmap scaling 36 solutions to halve emissions by 2030

Hereby a few examples,

Smart Grid

Turn the electricity system into a game. The board is the electricity grid, our pieces are energy-intensive devices on the grid, and our goal is to minimize the emissions produced by these devices whilst maintaining a quality of service to society.

Eliminate peaker plants which fire up when electricity demand is highest. Some of them operate only 40 hours a year. This nonsense remains nevertheless the reality in our Western countries.
Better scheduling to reduce long-distance transmission.
Smart EV charging to absorb excess wind energy.

Smart HVAC: Building energy

RL algorithms can reduce the energy consumption by interacting directly with the heating/cooling system. It can adapt its learning continuously to the controlled environment using real-time data collected on-site (external temperature, energy prices, occupancy, etc) without the need to access a thermo-energetic model of the building.

Smart Transportation

The proliferating RL solutions in transportation are already facilitating the transition.

From ownership to usership: The mobility as a service market offers options such as ride-hailing, ridesharing, carsharing, bikesharing, scooter-sharing. They all face complex ressources allocation problems in unseen environments in which RL is of great help.
To intelligent traffic signals to ease the vehicles flow.
To trucking optimisation, including efficient load , route, planning, etc.

Smart Food

For cultivated seafood companies, RL controls the environment parameters for optimum growth. This reduces the need to add external growth factors into the tissue, which is how most cultivated meats are grown today, water pollution, etc.

In a next blog post, we’ll explain in detail the case Heating-Ventilation-Air Conditioning (HVAC) control system with RL.