How can I expedite reinforcement learning training time in MATLAB?

I am working on reinforcement learning using the Reinforcement Learning Toolbox in MATLAB. I noticed that when I run training serially, the agent converges in fewer episodes (but still takes time). However, when I enable parallel training on my Dell Precision workstation, the agent requires significantly more episodes to achieve the same level of performance. I was expecting parallelization to speed up training, but instead it seems to increase sample requirements.

Any advice on how to optimize training speed in this kind of setup would be greatly appreciated.

Answer 1

[Umar]

Hi @Nida Ahsan,

Your observation is correct and well-documented. Parallel RL training trades sample efficiency for wall-clock time, but this trade-off often doesn't favor parallelization on workstation setups.

Note: I don't have RL Toolbox access - this is based on documentation research.

The issue is:

Parameter staleness: Workers collect experiences with outdated policy parameters while the central learner updates. This creates inconsistent learning signals, requiring more episodes to converge - exactly what you're observing.

Performance expectation mismatch: Parallel RL only speeds up wall-clock time when environment simulation is computationally expensive relative to network updates. Most standard environments don't meet this threshold.

I would recommend the following solutions:

1. Optimize worker count: Use 4-5 workers max on your Dell Precision (reserve cores for central learner coordination) 2. Switch to synchronous mode:parallelOpts = rlParallelizationOptions('Mode', 'sync');trainingOpts = rlTrainingOptions('UseParallel', true, 'ParallelizationOptions', parallelOpts); 3. Reduce mini-batch size: Improves computation-to-communication ratio for better sample efficiency 4. Monitor resource utilization: Check if CPU cores are actually being fully utilized during training

Speed Optimization Alternatives

• Multiple serial runs: Run 4 independent experiments with different seeds simultaneously - often faster than single parallel run
• GPU acceleration: If using deep networks, set UseDevice to 'gpu' in actor/critic options
• Hyperparameter adjustment: Reduce network complexity or increase learning rates when using parallel training

The bottom line is that your expectation was reasonable - parallelization should speed up training. However, RL's sample complexity makes this trade-off unfavorable for most workstation setups. Serial training typically converges faster in total episodes, while parallel training spreads the same learning across more episodes but potentially less wall-clock time.

What agent type and environment complexity are you working with? This determines whether parallel training makes sense for your case.