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Parameter staleness: Workers collect experiences with outdated policy parameters while the central learner updates.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.
