Distributed RL and Deployment

Standard RL can be painfully slow: one agent interacts with one environment and collects data step by step. Modern systems often separate data generation from learning. Actors generate trajectories; learners update model parameters.

Parallel rollout throughput often drives wall-clock progress:

$$\text{throughput}\approx (\#\text{ actors})\times(\text{steps per second per actor})$$

More actors increase both opportunity and risk. Engineering decisions include the number of actors, actor-learner communication protocol, frequency of syncing policies, replay-buffer capacity, compression, batching, and checkpoint cadence.

Important system constraints include network bandwidth, replay-buffer throughput bottlenecks, simulation CPU versus learner GPU balance, and whether adding actors produces stable wall-clock speedups.

Preference-based RL and RLAIF add another engineering pipeline on top of the usual RL loop:

The main engineering challenges are data quality, annotator variance, interface design, rater calibration, sampling diverse training examples, and keeping reward-model versions tied to the policies trained from them.

Deployment asks whether $\pi_{\text{deployed}}$ behaves like $\pi_{\text{trained}}$ under real noise distributions. The policy may have passed offline evaluation and still fail after compression, lower precision inference, sensor drift, latency, or a changed user population.

Deployment engineering often includes latency budgets, policy distillation for compression, monitoring drift in sensor inputs or prompts, retraining without catastrophic forgetting, safety fallback policies, and rollback safeguards. For fast control loops, the difference between a trained simulator policy and a deployed runtime policy may be only a few milliseconds wide.