options for training multiple reinforcement learning agents -凯发k8网页登录

agent grouping indices, specified as a cell array of positive integers or a cell array of integer arrays.

for instance, consider a training scenario with 4 agents. you can group the agents in the following ways:

agentgroups and learningstrategy must be used together to specify whether agent groups learn in a centralized manner or decentralized manner.

example: agentgroups={1,2,[3,4]}

learning strategy for each agent group, specified as either "decentralized" or "centralized". in decentralized training, agents collect their own set of experiences during the episodes and learn independently from those experiences. in centralized training, the agents share the collected experiences and learn from them together.

agentgroups and learningstrategy must be used together to specify whether agent groups learn in a centralized manner or decentralized manner. for example, you can use the following command to configure training for three agent groups with different learning strategies. the agents with indices [1,2] and [3,5] learn in a centralized manner, while agent 4 learns in a decentralized manner.

trainopts = rlmultiagenttrainingoptions(...
     agentgroups={[1,2],4,[3,5]},...
     learningstrategy=["centralized","decentralized","centralized"] )

example: learningstrategy="centralized"

maximum number of episodes to train the agents, specified as a positive integer. regardless of other criteria for termination, training terminates after maxepisodes.

example: maxepisodes=1000

maximum number of steps to run per episode, specified as a positive integer. in general, you define episode termination conditions in the environment. this value is the maximum number of steps to run in the episode if other termination conditions are not met.

example: maxstepsperepisode=1000

window length for averaging the scores, rewards, and number of steps for each agent, specified as a scalar or vector.

specify a scalar to apply the same window length to all agents. to use a different window length for each agent, specify scoreaveragingwindowlength as a vector. in this case, the order of the elements in the vector correspond to the order of the agents used during environment creation.

for options expressed in terms of averages, scoreaveragingwindowlength is the number of episodes included in the average. for instance, if stoptrainingcriteria is "averagereward" and stoptrainingvalue is 500 for a given agent then for that agent, training terminates when the average reward over the number of episodes specified in scoreaveragingwindowlength equals or exceeds 500. for the other agents, training continues until:

example: scoreaveragingwindowlength=10

training termination condition, specified as one of the following strings:

example: stoptrainingcriteria="averagereward"

critical value of the training termination condition, specified as a scalar or a vector.

specify a scalar to apply the same termination criterion to all agents. to use a different termination criterion for each agent, specify stoptrainingvalue as a vector. in this case, the order of the elements in the vector corresponds to the order of the agents used during environment creation.

for a given agent, training ends when the termination condition specified by the stoptrainingcriteria option equals or exceeds this value. for the other agents, the training continues until:

for instance, if stoptrainingcriteria is "averagereward" and stoptrainingvalue is 100 for a given agent, then for that agent training terminates when the average reward over the number of episodes specified in scoreaveragingwindowlength equals or exceeds 100.

example: stoptrainingvalue=100

condition for saving agents during training, specified as one of the following strings:

set this option to store candidate agents that perform well according to the criteria you specify. when you set this option to a value other than "none", the software sets the saveagentvalue option to 500. you can change that value to specify the condition for saving the agent.

for instance, suppose you want to store for further testing any agent that yields an episode reward that equals or exceeds 100. to do so, set saveagentcriteria to "episodereward" and set the saveagentvalue option to 100. when an episode reward equals or exceeds 100, train saves the corresponding agent in a mat-file in the folder specified by the saveagentdirectory option. the mat-file is called agentk.mat, where k is the number of the corresponding episode. the agent is stored within that mat-file as saved_agent.

example: saveagentcriteria="episodereward"

critical value of the condition for saving agents, specified as a scalar or a vector.

specify a scalar to apply the same saving criterion to each agent. to save the agents when one meets a particular criterion, specify saveagentvalue as a vector. in this case, the order of the elements in the vector corresponds to the order of the agents used when creating the environment. when a criteria for saving an agent is met, all agents are saved in the same mat-file.

when you specify a condition for saving candidate agents using saveagentcriteria, the software sets this value to 500. change the value to specify the condition for saving the agent. see the saveagentcriteria option for more details.

example: saveagentvalue=100

folder for saved agents, specified as a string or character vector. the folder name can contain a full or relative path. when an episode occurs that satisfies the condition specified by the saveagentcriteria and saveagentvalue options, the software saves the agents in a mat-file in this folder. if the folder does not exist, train creates it. when saveagentcriteria is "none", this option is ignored and train does not create a folder.

example: saveagentdirectory = pwd "\run1\agents"

option to stop training when an error occurs during an episode, specified as "on" or "off". when this option is "off", errors are captured and returned in the simulationinfo output of train, and training continues to the next episode.

example: stoponerror = "off"

option to display training progress on the command line, specified as the logical value false (0) or true (1). set to true to write information from each training episode to the matlab command line during training.

example: verbose = true

option to display training progress with episode manager, specified as "training-progress" or "none". by default, calling train opens the reinforcement learning episode manager, which graphically and numerically displays information about the training progress, such as the reward for each episode, average reward, number of episodes, and total number of steps. (for more information, see train.) to turn off this display, set this option to "none".

example: plots = "none"