RL Training Slows Down With Full Experience Buffer

I'm working on an RL project using a DQN agent with a custom environment. I tried training the agent without an LSTM layer with limited success and now I'm trying with an LSTM layer. I very quickly noticed that after the experience buffer is filled, i.e. the Total Number of Steps counter reaches the ExperienceBuffer length set in the agent properties, the training slows to a crawl. Where the first several training episodes complete in seconds, once this value of total steps is reached the next episode takes several minutes to train, and future ones don't seem to recover the speed. This hasn't happened in all my iterations of the agent and learning settings before I enabled the LSTM layer.

I'm wondering if this is expected behavior, why this might be happening given my settings, etc. My agent options are below, more details can be given if needed. Thanks!

agentOpts = rlDQNAgentOptions;
agentOpts.SequenceLength = 16;
agentOpts.MiniBatchSize = 100; % grab 100 "trajectories" of length 16 from the buffer at a time for training
agentOpts.NumStepsToLookAhead = 1; % forced for LSTM enabled critics
agentOpts.ExperienceBufferLength = 10000; % once the total steps reaches this number, training slows down dramatically

The critic network setup is:

ObservationInfo = rlNumericSpec([16 1]); % 16 tile game board
ActionInfo = rlFiniteSetSpec([1 2 3 4]); % 4 possible actions per turn

% Input Layer
layers = [
    sequenceInputLayer(16, "Name", "input_1")
    fullyConnectedLayer(1024,"Name","fc_1", "WeightsInitializer", "he")
    reluLayer("Name","relu_body")
    ];

% Body Hidden Layers
for i = 1:3
    layers = [layers 
        fullyConnectedLayer(1024,"Name", join(["fc_" num2str(i)]), "WeightsInitializer", "he")
        reluLayer("Name", join(["body_output_" num2str(i)]))
        ];
end

% LSTM Layer
layers = [layers
        lstmLayer(1024,"Name","lstm", "InputWeightsInitializer", "he", "RecurrentWeightsInitializer", "he")
    ];

% Output Layer
layers = [layers 
    fullyConnectedLayer(4,"Name","fc_action")
    regressionLayer("Name","output")
    ];

dqnCritic = rlQValueRepresentation(layers, ObservationInfo, ActionInfo, "Observation", "input_1");

[Tech Logg Ding]

I am also facing the same problem. Have you found a solution to this problem?

I suspect that it could be improved by reducing the sequence length and mini batch size parameters, but yours already looks quite small.

Thank you.

[Giancarlo Storti Gajani]

I have a similar problem, but training slows down after a number of steps much smaller than the experience buffer lenght.

Initially each episode takes about 0.5s, after some iterations (about 1000) episodes need more than 30s to complete. I am using a 128 sized minibatch and 1e6 experience buffer length.

[轩]

Same question and still waiting the answer.....

In my training example, the slow down speed seems to be linear, it doesn't about if my experience buffer is reach to be filled.

And if you do the training without opening the simulink model window, training will be more faster