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Train Reinforcement Learning Agents

Once you have created an environment and reinforcement learning agent, you can train the agent in the environment using the train function. To configure your training, use an rlTrainingOptions object. For example, create a training option set opt, and train agent agent in environment env.

opt = rlTrainingOptions(...
trainResults = train(agent,env,opt);

If env is a multi-agent environment specify the agent argument as an array. The order of the agents in the array must match the agent order used to create env. For multiagent training, use rlMultiAgentTrainingOptions instead of rlTrainingOptions. Using rlMultiAgentTrainingOptions gives you access to training options that are specific to multiagent training. For more information about multiagent training, see Multiagent Training.

For more information on creating agents, see Reinforcement Learning Agents. For more information on creating environments, see Reinforcement Learning Environments and Create Custom Simulink Environments.


train updates the agent as training progresses. This is possible because agents are handle objects. To preserve the original agent parameters for later use, save the agent to a MAT-file:

If you copy the agent into a new variable, the new variable will also always point to the most recent agent version with updated parameters. For more information about handle objects, see Handle Object Behavior.

Training terminates automatically when the conditions you specify in the StopTrainingCriteria and StopTrainingValue options of your rlTrainingOptions object are satisfied. You can also terminate training before any termination condition is reached by clicking Stop Training in Reinforcement Learning Training Monitor.

When training terminates the training statistics and results are stored in the trainResults object.

Because train updates the agent at the end of each episode, and because trainResults stores the last training results along with data to correctly recreate the training scenario and update Reinforcement Learning Training Monitor, you can later resume training from the exact point at which it stopped. To do so, at the command line, type:

trainResults = train(agent,env,trainResults);
This starts the training from the last values of the agent parameters and training results object obtained after the previous train call.

The TrainingOptions property of trainResults contains the rlTrainingOptions object that specifies the training option set. Therefore, to restart the training with updated training options, first change the training options in trainResults using dot notation. If the maximum number of episodes was already reached in the previous training session, you must increase the maximum number of episodes.

For example, disable displaying the training progress on Reinforcement Learning Training Monitor, enable the Verbose option to display training progress at the command line, change the maximum number of episodes to 2000, and then restart the training, returning a new trainResults object as output.

trainResults.TrainingOptions.MaxEpisodes = 2000;
trainResults.TrainingOptions.Plots = "none";
trainResults.TrainingOptions.Verbose = 1;
trainResultsNew = train(agent,env,trainResults);


When training terminates, agent reflects the state of each agent at the end of the final training episode. The rewards obtained by the final agents are not necessarily the highest achieved during the training process, due to continuous exploration. To save agents during training, create an rlTrainingOptions object specifying the SaveAgentCriteria and SaveAgentValue properties and pass it to train as a trainOpts argument. For information on how to evaluate agent, see Evaluate Agents During Training.

Training Algorithm

In general, training performs the following steps.

  1. Initialize the agent.

  2. For each episode:

    1. Reset the environment.

    2. Get the initial observation s0 from the environment.

    3. Compute the initial action a0 = μ(s0), where μ(s) is the current policy.

    4. Set the current action to the initial action (aa0), and set the current observation to the initial observation (ss0).

    5. While the episode is not finished or terminated, perform the following steps.

      1. Apply action a to the environment and obtain the next observation s''and the reward r.

      2. Learn from the experience set (s,a,r,s').

      3. Compute the next action a' = μ(s').

      4. Update the current action with the next action (aa') and update the current observation with the next observation (ss').

      5. Terminate the episode if the termination conditions defined in the environment are met.

  3. If the training termination condition is met, terminate training. Otherwise, begin the next episode.

The specifics of how the software performs these steps depend on the configuration of the agent and environment. For instance, resetting the environment at the start of each episode can include randomizing initial state values, if you configure your environment to do so. For more information on agents and their training algorithms, see Reinforcement Learning Agents. To use parallel processing and GPUs to speed up training, see Train Agents Using Parallel Computing and GPUs.

Reinforcement Learning Training Monitor

By default, calling the train function opens the Reinforcement Learning Training Monitor, which lets you visualize the training progress.

Reinforcement Learning Training Monitor window showing the completion of the training for a DQN agent on the predefined pendulum environment.

The Reinforcement Learning Training Monitor plot shows the reward for each episode (EpisodeReward) and a running average reward value (AverageReward).

For agents with a critic, Episode Q0 is the estimate of the discounted long-term reward at the start of each episode, given the initial observation of the environment. As training progresses, if the critic is well designed and learns successfully, Episode Q0 approaches in average the true discounted long-term reward, which may be offset from the EpisodeReward value because of discounting. For a well designed critic using an undiscounted reward (DiscountFactor is equal to 1), then on average Episode Q0 approaches the true episode reward, as shown in the preceding figure.

The Reinforcement Learning Training Monitor also displays various episode and training statistics. You can also use the train function to return episode and training information. To prevent displaying the training information with the Reinforcement Learning Training Monitor, set the Plots option of rlTrainingOptions to "none".

Save Candidate Agents

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 example, save agents when the episode reward is greater than 100.

opt = rlTrainingOptions(SaveAgentCriteria="EpisodeReward",SaveAgentValue=100);

train stores saved agents in a MAT-file in the folder you specify using the SaveAgentDirectory option of rlTrainingOptions. Saved agents can be useful, for instance, to test candidate agents generated during a long-running training process. For details about saving criteria and saving location, see rlTrainingOptions.

After training is complete, you can save the final trained agent from the MATLAB® workspace using the save function. For example, save the agent myAgent to the file finalAgent.mat in the current working directory.

save(opt.SaveAgentDirectory + "/finalAgent.mat",'agent')

By default, when DDPG and DQN agents are saved, the experience buffer data is not saved. If you plan to further train your saved agent, you can start training with the previous experience buffer as a starting point. In this case, set the SaveExperienceBufferWithAgent option to true. For some agents, such as those with large experience buffers and image-based observations, the memory required for saving the experience buffer is large. In these cases, you must ensure that enough memory is available for the saved agents.

Validate Trained Policy

To validate your trained agent, you can simulate the agent within the training environment using the sim function. To configure the simulation, use rlSimulationOptions.

When validating your agent, consider checking how your agent handles the following:

As with parallel training, if you have Parallel Computing Toolbox™ software, you can run multiple parallel simulations on multicore computers. If you have MATLAB Parallel Server™ software, you can run multiple parallel simulations on computer clusters or cloud resources. For more information on configuring your simulation to use parallel computing, see UseParallel and ParallelizationOptions in rlSimulationOptions.

Address Memory Issues During Training

Some Simulink® environments save a considerable amount of data when running. Specifically, by default, the software saves anything that appears as the output of a sim (Simulink) command. This can cause out-of-memory issues when training or simulating an agent in this kind of environment. You can use three training (or simulation) options to prevent memory-related problems:

  • SimulationStorageType — This option specifies the type of storage used for data generated during training or simulation by a Simulink environment. The default value is "memory", indicating that data is stored in memory. To store environment data to disk instead, set this option to "file". When this option is set to "none" environment data is not stored.

  • SaveSimulationDirectory — This option specifies the directory to save environment data when SimulationStorageType is set to "file". The default value is "savedSims".

  • SaveFileVersion — This option specifies the MAT-file version for environment data. The default is "-v7". The other possible options are "-v7.3" and "-v6".

For more information, see rlTrainingOptions, rlMultiAgentTrainingOptions, rlEvolutionStrategyTrainingOptions, and rlSimulationOptions.

Environment Visualization

If your training environment implements the plot method, you can visualize the environment behavior during training and simulation. If you call plot(env) before training or simulation, where env is your environment object, then the visualization updates during training to allow you to visualize the progress of each episode or simulation.

Environment visualization is not supported when training or simulating your agent using parallel computing.

For custom environments, you must implement your own plot method. For more information on creating a custom environments with a plot function, see Create Custom Environment from Class Template.

Evaluate Agents During Training

You can automatically evaluate your agent at regular intervals during training. Doing so allows you to observe the actual training progress and automatically stop the training or save the agent when some pre-specified conditions are met.

To configure evaluation options for your agents, first create an evaluator object using rlEvaluator. You can specify properties such as the type of evaluation statistic, the frequency at which evaluation episodes occur, or whether exploration is allowed during an evaluation episode.

To train the agents and evaluate them during training, pass this object to train.

You can also create a custom evaluator object, which uses a custom evaluation function that you supply. To do so, use rlCustomEvaluator.

For more information, see also the EvaluationStatistic of train.

Offline Training

You can train off-policy agents (DQN, SAC, DDPG, and TD3) offline, using an existing dataset, instead of an environment.

To train your agent from an existing dataset, first, use rlTrainingFromDataOptions to create a training from data option object. Then pass this option object (along with the environment and agent) to trainFromData to train your agent.

To deal with possible differences between the probability distribution of the dataset and the one generated by the environment, use the batch data regularization options provided for off-policy agents. For more information, see the new BatchDataRegularizerOptions property of the off-policy agents options objects, as well as the new rlBehaviorCloningRegularizerOptions and rlConservativeQLearningOptions options objects.

Using Evolutionary Strategies

You can train DDPG, TD3 and SAC agents using an evolutionary algorithm.

Evolutionary reinforcement learning adaptation strategies update the weights of your agents using a selection process inspired by biological evolution. Compared to gradient-based approaches, evolutionary algorithms are less reliant on backpropagation, are easily parallelizable, and have a reduced sensitivity to local minima. They also generally display good (nonlocal) exploration and robustness, especially in complex scenarios where data is incomplete or noisy and rewards are sparse or conflicting.

To train your agent using an evolutionary algorithm, first, use rlEvolutionStrategyTrainingOptions to create a evolution strategy training option object. Then pass this option object (along with the environment and agent) to trainWithEvolutionStrategy to train your agent.

For an example, see .

Multiagent Training

You can create and train multiple agents that work together in the same environment. To do so, first, create a multiagent environment.

Using MATLAB functions, you can create two different kinds of custom multiagent environments:

  • Multiagent environments with universal sample time, in which all agents execute in the same step. You can create these environments by supplying your own reset and step functions, as well as observations and action specifications, to rlMultiAgentFunctionEnv.

  • Turn-based function environments, in which agents execute in turns. Specifically, the environment assigns execution to only one group of agents at a time, and the group executes when it is its turn to do so. You can create these environments by supplying your own reset and step functions, as well as observations and action specifications, to rlTurnBasedFunctionEnv. For an example, see Train Agent to Play Turn-Based Game.

For both kinds of multiagent environments, the observation and action specifications are cell arrays of specification objects in which each element corresponds to one agent.

You can also create a custom multiagent Simulink environment (this option allows you to model environments with multi-rate execution, in which each agent may have its own execution rates).

To create a custom multiagent Simulink, first, create a Simulink model that has one action input and one set of outputs (observation, reward and is-done) for every agent. Then manually add an agent block for each agent. Once you connect the blocks, create an environment object using rlSimulinkEnv. Unless each agent block already references an agent object in the MATLAB workspace, you must supply to rlSimulinkEnv two cell arrays containing the observation action specification objects, respectively, as input arguments. For an example, see Train Multiple Agents to Perform Collaborative Task.

Once you have created your multiagent environment, specify options for multiagent training by creating and configuring a rlMultiAgentTrainingOptions object. Doing so allows you to specify, for example, whether different groups of agents are trained in a decentralized or centralized manner. In a group of agents subject to decentralized training, each agent collects its own set of experiences and learns from its own set of experiences. In a group of agents subject to centralized training, each agent shares its experiences with the other agents in the group and each agent in the group learns from the collective shared experiences. In general, centralized learning boosts exploration and facilitates learning in applications where the agents perform a collaborative (or the same) task.

Once you have your multiagent environment and multiagent training options object, you can train and simulate your agents with is using train and sim, respectively. You can visualize the training progress of all the agents using the Reinforcement Learning Training Manager.

For more examples on training multiple agents, see also Train Multiple Agents for Area Coverage, and Train Multiple Agents for Path Following Control.

Why is Training not Converging?

In general, reinforcement learning algorithms cannot be guaranteed to converge even to a vicinity of a local optimum, unless many assumptions on the environment, the algorithm, and the function approximator are verified. Typically such assumption involve availability of the environment state, finite state and action spaces, known dynamics, tabular or linear function approximation, appropriate (and decaying) exploration and learning rate.

In practice, there might be many reasons why training does not converge to a satisfying solution.

  • Insufficient exploration: If the agent does not explore different actions and states adequately, it may not discover the optimal policy. Insufficient exploration can lead to suboptimal results or getting stuck in local optima.

  • High-dimensional state or action spaces: When the state or action space is large, it becomes challenging for the agent to explore and learn effectively. The curse of dimensionality can make it difficult to find an optimal policy within a reasonable time frame.

  • Function approximation limitations: It is important to consider whether your function approximator can properly approximate your value functions or policy, while at the same time being able to properly generalize from experience, otherwise, convergence issues can arise. Note that unavoidable approximation errors arise when observation signals are not adequately selected. For more information on selecting good observation signals, see the observation section of Define Reward and Observation Signals in Custom Environments.

  • Improperly scaled observation or action signals: Signals with widely different ranges can skew the learning process, making it hard for your approximator to successfully learn the important features. To prevent scaling problems, normalize the observations to a consistent scale. Common normalization techniques include scaling the observations to the range [0, 1] or standardizing them with zero mean and unit variance.

  • Poorly designed reward signal: The reward signal plays a crucial role in reinforcement learning. If the rewards are not properly designed or do not reflect the desired behavior, the agent may struggle to learn the optimal policy. Inconsistent or sparse rewards can make the learning process unstable or slow. For more information on selecting good reward signals, see the rewards section of Define Reward and Observation Signals in Custom Environments.

  • Inadequate training time or resources: Reinforcement learning algorithms often require extensive training time and computational resources to converge. If the training time or resources are limited, the algorithm may not have sufficient iterations to converge to the optimal policy. For more information on using parallel computing to train agents, see Train Agents Using Parallel Computing and GPUs.

  • Inappropriate algorithm or hyperparameters: Choosing an inappropriate algorithm or setting incorrect hyperparameters can significantly impact convergence. Different algorithms and hyperparameters may be more suitable for specific problem domains, and selecting the wrong ones can hinder convergence. For more information on selecting agents, see the last section of Reinforcement Learning Agents.

  • Non-stationary environments: If the environment in which the agent operates changes over time, the learning algorithm may struggle to adapt. Non-stationary environments can introduce additional challenges, requiring the algorithm to continuously update its policy to account for the changes.

To address these convergence issues, you can try to adjust the exploration-exploitation trade-off, use better reward shaping, fine-tune hyperparameters, use an experience replay, or rely on more advanced algorithms.


Choosing the right observation and action sets is crucial for effective training and performance in reinforcement learning. The observations should provide sufficient information about the current environment state for the agent to make informed decisions, and the actions should be able to adequately steer the environment behavior. For more information on selecting good observation and reward signals, see Define Reward and Observation Signals in Custom Environments.

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