plot training information from a previous training session -凯发k8网页登录

for this example, assume that you have trained the agent in the train reinforcement learning agent in mdp environment example and subsequently closed the episode manager.

load the training information returned by the train function.

load mdptrainingstats trainingstats

reopen the episode manager for this training session.

inspecttrainingresult(trainingstats)

for this example, load the environment and agent for the train reinforcement learning agent in mdp environment example.

load mdpagentandenvironment

specify options for training the agent. configure the saveagentcriteria and saveagentvalue options to save all agents after episode 30.

trainopts = rltrainingoptions;
trainopts.maxstepsperepisode = 50;
trainopts.maxepisodes = 50;
trainopts.plots = "none";
trainopts.saveagentcriteria = "episodecount";
trainopts.saveagentvalue = 30;

train the agent. during training, when an episode has a reward greater than or equal to 13, a copy of the agent is saved in a savedagents folder.

rng("default") % for reproducibility
trainingstats = train(qagent,env,trainopts);

load the training results for one of the saved agents. this command loads both the agent and a structure that contains the corresponding training results.

load savedagents/agent50

view the training results from the saved agent result structure.

inspecttrainingresult(savedagentresult)

the episode manager shows the training progress up to the episode in which the agent was saved.