Analysis of fourteen recent Reinforcement Learning Publications
In the realm of clean energy production, reinforcement learning (RL) is making significant strides, particularly in the control of tokamak plasmas for fusion energy.
A study published in Nature by researchers from DeepMind has employed RL to control the fusion of a hydrogen plasma in a tokamak, a significant advance in clean energy production [1]. The challenge was to adjust the magnetic fields of the tokamak precisely and rapidly to prevent the plasma from touching the walls, a problem that the learned model successfully solved. This continuous control is more sophisticated than traditional step-based control systems, allowing for real-time adaptive management of plasma behavior [4][5].
Collaborations like EPFL and DeepMind have developed magnetic control methods based on deep reinforcement learning and tested them on simulators and real-world tokamaks like TCV. These systems optimise control variables rapidly, avoiding long calculation times typical of conventional methods [5].
Beyond magnetic control, reinforcement learning complements neural network–based approaches that model plasma dynamics and derive optimal external source profiles (e.g., neutral beam injection power) necessary to reach and maintain target plasma core densities and temperatures. This combined approach uses machine learning to transform simulation tools into control-oriented models, thereby enabling precise regulation of energy input to sustain burning plasma states crucial for fusion energy production [2][3].
In a separate development, the paper "A Generalist Agent" describes GATO, a model capable of performing a wide variety of tasks, including playing Atari games, chatting, captioning pictures, and controlling a robotic arm [6]. GATO uses a transformer neural network to process serialized data into a sequence of tokens. The Decision Transformer model, another RL approach, takes past states, actions, and desired returns to generate future actions [7].
The Decision Transformer performs as well, or even better, than state-of-the-art model-free offline RL baselines on various tasks [7]. Interestingly, GATO, while being the first model with such a high level of generality, is poor in all tasks compared to expert models in the literature [6].
The papers reviewed demonstrate the versatility and potential of reinforcement learning for solving a wide range of problems across different domains. Another noteworthy point is the proposal of a shift towards paradigms where the agent sets its own goals in self-supervised learning and reinforcement learning [8]. The Decision Transformer avoids the "deadly triad" and discounting future rewards, and uses existing transformer frameworks and supervised learning systems [7].
In conclusion, reinforcement learning provides a powerful, adaptive control framework that helps manage the complex, nonlinear plasma behavior in tokamaks, making clean fusion energy production more feasible by maintaining stable, efficient plasma confinement and burn conditions [1][4][5]. Meanwhile, the development of generalist agents like GATO and the Decision Transformer underscores the versatility of RL in tackling a wide array of problems, from clean energy to robotics and beyond.
Artificial-intelligence, in the form of deep reinforcement learning, is being applied not only to control clean energy production through plasma fusion in tokamaks but also to create generalist agents like GATO, capable of performing various tasks.
The Decision Transformer model, an RL approach, has shown remarkable performance in a variety of tasks, foreshadowing the extensive applicability of artificial-intelligence in numerous domains.
