Once you train a reinforcement learning agent, you can generate code to deploy the optimal policy. You can generate:

CUDA

^{®}code for deep neural network policies using GPU Coder™C/C++ code for table, deep neural network, or linear basis function policies using MATLAB

^{®}Coder™

Code generation for deep neural network policies supports only networks with a single input layer.

For more information on training reinforcement learning agents, see Train Reinforcement Learning Agents.

To generate code for the trained optimal policy of a reinforcement learning agent, you must first create a policy evaluation function for the agent. You can generate a policy function for an agent with any type of policy representation object:

Value and Q tables (

`rlTableRepresentation`

)Deep neural networks (

`rlLayerRepresentation`

)Linear basis functions (

`rlLinearBasisRepresentation`

)

For more information on the different types of policies, see Create Policy and Value Function Representations.

To create a policy evaluation function that selects an action based on a given
observation, use the `generatePolicyFunction`

command. This command generates a MATLAB script, which contains the policy evaluation function, and a MAT-file, which
contains the optimal policy data.

You can generate code to deploy this policy function using GPU Coder or MATLAB Coder.

If your trained optimal policy uses a deep neural network, you can generate CUDA code for the policy using GPU Coder. There are several required and recommended prerequisite products for generating CUDA code for deep neural networks. For more information, see Installing Prerequisite Products (GPU Coder) and Setting Up the Prerequisite Products (GPU Coder).

Not all deep neural network layers support GPU code generation. For a list of supported layers, see Supported Networks and Layers (GPU Coder). For more information and examples on GPU code generation, see Deep Learning with GPU Coder (GPU Coder).

As an example, generate GPU code for the policy gradient agent trained in Train PG Agent to Balance Cart-Pole System.

Load the trained agent.

load('MATLABCartpolePG.mat','agent')

Create a policy evaluation function for this agent.

generatePolicyFunction(agent)

This command creates the `evaluatePolicy.m`

file, which contains
the policy function, and the `agentData.mat`

file, which contains the
trained deep neural network actor. For a given observation, the policy function
evaluates a probability for each potential action using the actor network. Then, the
policy function randomly selects an action based on these probabilities.

Since the actor network for this PG agent has a single input layer and single output layer, you can generate code for this network using GPU Coder. For example, you can generate a CUDA compatible MEX function.

Configure the `codegen`

function to create a CUDA compatible C++ MEX function.

cfg = coder.gpuConfig('mex'); cfg.TargetLang = 'C++'; cfg.DeepLearningConfig = coder.DeepLearningConfig('cudnn');

Set the dimensions of the policy evaluation input argument, which correspond to
the observation specification dimensions for the agent. To find the observation
dimensions, use the `getObservationInfo`

function. In this case,
the observations are in a four-element vector.

`argstr = '{ones(4,1)}';`

Generate code using the `codegen`

function.

codegen('-config','cfg','evaluatePolicy','-args',argstr,'-report');

This command generates the MEX function
`evaluatePolicy_mex`

.

You can generate C/C++ code for table, deep neural network, or linear basis function policies using MATLAB Coder.

Using MATLAB Coder, you can generate:

C/C++ code for policies that use Q tables, value tables, or linear basis functions. For more information on general C/C++ code generation, see Generating Code (MATLAB Coder).

C++ code for policies that use deep neural networks. For more information, see Prerequisites for Deep Learning with MATLAB Coder (MATLAB Coder) and Deep Learning with MATLAB Coder (MATLAB Coder).

As an example, generate C code for the Q-learning agent trained in Train Reinforcement Learning Agent in Basic Grid World.

Load the trained agent.

load('basicGWQAgent.mat','qAgent')

Create a policy evaluation function for this agent.

generatePolicyFunction(qAgent)

This command creates the `evaluatePolicy.m`

file, which contains
the policy function, and the `agentData.mat`

file, which contains the
trained Q table value function. For a given observation, the policy function looks up
the value function for each potential action using the Q table. Then, the policy
function selects the action for which the value function is greatest.

Set the dimensions of the policy evaluation input argument, which correspond to
the observation specification dimensions for the agent. To find the observation
dimensions, use the `getObservationInfo`

function. In this case,
there is a single finite observation.

`argstr = '{[1]}';`

Configure the `codegen`

function to generate embeddable C code
suitable for targeting a static library, and set the output folder to
`buildFolder`

.

cfg = coder.config('lib'); outFolder = 'buildFolder';

Generate C code using the `codegen`

function.

codegen('-c','-d',outFolder,'-config','cfg',... 'evaluatePolicy','-args',argstr,'-report');