How to save a RL agent after training and then further train it?

My agent is taking too much time in running a large number of episodes. So I want to train it multiple times, but with a small number of episodes everytime. I require the system to load all values of experience buffer and weights each time when I load my agent for further training. My agent is DDPG.

Answer 1

[Ronit]

Hello Sania,

To save and load a Deep Deterministic Policy Gradient (DDPG) agent for further training, you need to save the agent's weights and the experience buffer. This can be done using MATLAB's built-in functions for saving and loading objects.

• Use the "save" function to save the agent object to a ".mat" file. This will save all properties of the agent, including the experience buffer and the neural network weights.

save('trainedDDPGAgent.mat', 'agent');

• Use the "load" function to load the agent object from the ".mat" file.

loadedData = load('trainedDDPGAgent.mat', 'agent');
agent = loadedData.agent;

• Use the loaded agent to continue training for more episodes.

% Define your environment and training options
env = ...;
trainingOptions = ...;

% Continue training the loaded agent
agent = train(agent, env, trainingOptions);

• Ensure that the environment "env" is exactly the same as the one used during the initial training. Any changes in the environment can affect the training process.
• The experience buffer is part of the agent object and will be saved and loaded with it. Ensure that the buffer size and other related parameters are consistent.

Please refer to the highlighted section in the following MATLAB documentation for more information:

https://www.mathworks.com/help/reinforcement-learning/ug/train-reinforcement-learning-agents.html#:~:text=After%20training%20is,is%20not%20saved

I hope this resolves your query!

[Sania Gul]

Tnk u sooooooooo much, :-)