Principle:Farama Foundation Gymnasium Synchronous Vector Execution

Knowledge Sources	SyncVectorEnv
Domains	Reinforcement_Learning, Parallelism
Last Updated	2026-02-15 03:00 GMT

Overview

A vectorization strategy that runs multiple environment instances sequentially in a single process, trading throughput for simplicity and debuggability.

Description

Synchronous Vector Execution steps through each sub-environment one at a time within the main process. While slower than asynchronous execution for expensive environments, it provides:

• Debuggability: Standard Python debugging tools work without multiprocessing complications
• Determinism: Fully deterministic execution order
• Low overhead: No inter-process communication or shared memory management
• Compatibility: Works with any environment, including those with non-picklable state

It is the preferred mode for lightweight environments (tabular, simple physics) where environment step time is negligible compared to network inference time.

Usage

Use synchronous execution when environments are lightweight, when debugging vectorized training loops, or when environments have non-serializable state that prevents multiprocessing.

Theoretical Basis

Sequential execution of $N$ environments:

# Abstract algorithm
def sync_step(envs, actions):
    results = []
    for env, action in zip(envs, actions):
        results.append(env.step(action))  # Sequential
    return batch(results)

Total wall time: $T_{total}=N\times T_{env}+T_{agent}$

Related Pages

Implemented By

• Implementation:Farama_Foundation_Gymnasium_SyncVectorEnv

Uses Heuristic

• Heuristic:Farama_Foundation_Gymnasium_Sync_Vs_Async_VectorEnv_Selection