I understand that you want to know about saving both the agent and environment while training. During training, you can save candidate agents that meet conditions you specify in the ‘SaveAgentCriteria’ and ‘SaveAgentValue’ options of your ‘rlTrainingOptions’ object. For instance, you can save any agent whose episode reward exceeds a certain value, even if the overall condition for terminating training is not yet satisfied.
For more information, you can refer to the documentation link below –
