CONTROLLING A MAGNETIC FIELD OF A MAGNETIC CONFINEMENT DEVICE USING A NEURAL NETWORK

1 . A method performed by one or more data processing apparatus for generating control signals for controlling a magnetic field for confining plasma in a chamber of a magnetic confinement device, the method comprising, at each of a plurality of time steps:

obtaining an observation characterizing a current state of the plasma in the chamber of the magnetic confinement device;

processing an input comprising the observation characterizing the current state of the plasma in the chamber of the magnetic confinement device using a plasma confinement neural network, wherein the plasma confinement neural network has a plurality of network parameters and is configured to process the input comprising the observation in accordance with the network parameters to generate a magnetic control output that characterizes control signals for controlling the magnetic field of the magnetic confinement device; and

generating the control signals for controlling the magnetic field of the magnetic confinement device based on the magnetic control output.

2 . The method of claim 1 , wherein the magnetic control output characterizes a respective voltage to be applied to each of a plurality of control coils of the magnetic confinement device.

3 . The method of claim 2 , wherein the magnetic control output defines, for each of the plurality of control coils of the magnetic confinement device, a respective score distribution over a set of possible voltages that can be applied to the control coil.

4 . The method of claim 3 , wherein generating control signals for controlling the magnetic field of the magnetic confinement device based on the magnetic control output comprises, for each of the plurality of control coils of the magnetic confinement device:

selecting a voltage from the respective score distribution over the set of possible voltages that can be applied to the control coil; and

generating a control signal to cause the sampled voltage to be applied to the control coil.

5 . The method of claim 1 , further comprising:

determining, for each of the plurality of time steps, a reward for the time step that characterizes an error between: (i) the current state of the plasma, and (ii) a target state of the plasma; and

training the neural network parameters of the plasma confinement neural network on the rewards using a reinforcement learning technique.

6 . The method of claim 5 , wherein for one or more of the plurality of time steps, determining the reward for the time step comprises:

determining, for each of one or more plasma features characterizing the plasma, a respective error that measures a difference between: (i) a current value of the plasma feature at the time step, and (ii) a target value of the plasma feature at the time step; and

determining the reward for the time step based at least in part on the respective error corresponding to each of the one or more plasma features at the time step.

7 . The method of claim 6 , wherein for one or more of the plurality of time steps, determining the reward for the time step based on the respective error corresponding to each of the plasma features at the time step comprises:

determining the reward for the time step as a weighted linear combination of the respective errors corresponding to the plasma features at the time step.

8 . The method of claim 6 , wherein the respective target values of each of one or more of the plasma features vary between time steps.

9 . The method of claim 6 , wherein at each of the plurality of time steps, the input to the plasma confinement neural network includes data defining the respective target value of each of the plasma features at the time step in addition to the observation for the time step.

10 . The method of claim 6 , wherein the plasma features comprise one or more of: a stability of the plasma, a plasma current of the plasma, a shape of the plasma, a position of the plasma, an area of the plasma, a number of domains of the plasma, a distance between droplets of plasma, an elongation of the plasma, a radial position of a plasma center, a radius of the plasma, a triangularity of the plasma, or a limit point of the plasma.

11 . The method of claim 5 , wherein for one or more of the plurality of time steps, determining the reward for the time step comprises:

determining a respective current value of each of one or more device features characterizing a current state of the magnetic confinement device; and

determining the reward for the time step based at least in part on the respective current values of the one or more device features at the time step.

12 . The method of claim 11 , wherein the device features comprise: a number of x-points in the chamber of the magnetic confinement device, a respective current in each of one or more control coils of the magnetic confinement device, or both.

13 . The method of claim 1 , wherein the magnetic confinement device is a simulation of a magnetic confinement device, and further comprising, at a final time step of the plurality of time steps:

determining that a physical feasibility constraint of the magnetic confinement device is violated at the time step; and

terminating the simulation of the magnetic confinement device in response to determining that the physical feasibility constraint of the magnetic confinement device is violated at the time step.

14 . The method of claim 13 , wherein determining that the physical feasibility constraint of the magnetic confinement device is violated at the time step comprises one or more of: determining that a density of the plasma at the time step does not satisfy a threshold, determining that a plasma current of the plasma at the time step does not satisfy a threshold, or determining that a respective current in each of one or more of the control coils does not satisfy a threshold.

15 . The method of claim 5 , wherein the reinforcement learning technique is an actor-critic reinforcement learning technique, and wherein training the network parameters of the plasma confinement neural network on the rewards comprises:

jointly training the plasma confinement neural network and a critic neural network on the rewards using the actor-critic reinforcement learning technique, wherein the critic neural network is configured to process an input comprising a critic observation for a time step to generate an output that characterizes a cumulative measure of rewards that are predicted to be received after the time step.

16 . The method of claim 15 , wherein the actor-critic reinforcement learning technique is a maximum a posteriori policy optimization (MPO) technique.

17 . The method of claim 15 , wherein the actor-critic reinforcement learning technique is a distributed actor-critic reinforcement learning technique.

18 . The method of claim 15 , wherein the plasma confinement neural network generates outputs using fewer computational resources than are required by the critic neural network to generate outputs.

19 .- 28 . (canceled)

29 . A system comprising:

one or more computers; and

one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations for generating control signals for controlling a magnetic field for confining plasma in a chamber of a magnetic confinement device, the operations comprising, at each of a plurality of time steps:

obtaining an observation characterizing a current state of the plasma in the chamber of the magnetic confinement device;

processing an input comprising the observation characterizing the current state of the plasma in the chamber of the magnetic confinement device using a plasma confinement neural network, wherein the plasma confinement neural network has a plurality of network parameters and is configured to process the input comprising the observation in accordance with the network parameters to generate a magnetic control output that characterizes control signals for controlling the magnetic field of the magnetic confinement device; and

generating the control signals for controlling the magnetic field of the magnetic confinement device based on the magnetic control output.

30 . (canceled)

31 . One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for generating control signals for controlling a magnetic field for confining plasma in a chamber of a magnetic confinement device, the operations comprising, at each of a plurality of time steps:

obtaining an observation characterizing a current state of the plasma in the chamber of the magnetic confinement device;

processing an input comprising the observation characterizing the current state of the plasma in the chamber of the magnetic confinement device using a plasma confinement neural network, wherein the plasma confinement neural network has a plurality of network parameters and is configured to process the input comprising the observation in accordance with the network parameters to generate a magnetic control output that characterizes control signals for controlling the magnetic field of the magnetic confinement device; and

generating the control signals for controlling the magnetic field of the magnetic confinement device based on the magnetic control output.