cs.DCNov 5, 2026

SEER: A Scalable Event-Driven Runtime for Parallel Execution of Multi-Agent Reinforcement Learning Environments

Authors: Jiming Su, Feng Zhu, Yuhao Xiao, Miao Zhang

Organizations: NUDT

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Abstract

Multi-agent reinforcement learning (MARL) is increasingly used in systems where many entities interact in a shared environment. In these workloads, policy inference can be batched on GPUs, but the CPU-side environment path still updates entity states, resolves local interactions, and builds observations inside each env.step\mathrm{env.step}. This path can dominate latency in interaction-intensive workloads after policy batching. Existing vectorized environment interfaces and general event-driven runtimes do not directly address the fine-grained cross-entity work inside one bounded MARL step while preserving the step-level semantics expected by learning systems. We present SEER (Scalable Event-Driven Environment Runtime), a runtime for MARL environment execution under an explicit event-locality contract. SEER models each environment step as a bounded stream of timestamped events. If direct state updates are entity-local and cross-entity effects are represented as ordered future events, SEER can run independent events in parallel while preserving serial-equivalent boundary outputs. To make this contract practical, SEER uses a CAS-based dual-layer event queue: local event queues preserve per-entity causal order, while a parallel executive list exposes ready work to global worker threads. SEER further uses STPA, a preflight-guided thread-pool selection mechanism that combines short runtime traces with static workload and hardware features. We evaluate SEER on two parameterized MARL workloads: compute-intensive UAV swarm control (Aircraft) and communication-intensive wireless management (PCS). On Aircraft, SEER reduces inference latency by 16.2% over the baseline. On PCS, SEER reduces environment-execution latency by 46.9%, 19.5%, and 64.9% over ROSS-T, NBSE, and ROOT-Sim, respectively.

Figures & tables

Figure 1: MARL environment-execution workflow.
Figure 2: Single-step inference-time distribution in representative MARL environment scenarios. The policy network is MAPPO ( Yu et al. 2022 ) , and one agent corresponds to one environment entity.
Figure 3: End-to-end MARL inference-throughput ratio between ROSS-T and MAgent2 in the motivation-study workloads. The ratio is computed as ROSS-T throughput divided by MAgent2 throughput under the same motivation-study configuration. Event time denotes the average execution time of events within one environment step. These workloads differ from those used in the main evaluation.
Figure 4: SEER CAS-based dual-layer event queue for MARL environment execution.
Figure 5: STPA thread-pool prediction for event-driven MARL environment execution.
Figure 6: Architecture of the SEER event-driven environment-execution runtime.
Figure 7: Absolute environment-execution throughput of ROSS-T, SEER w/o STPA, and SEER under different event granularities in the Aircraft workload on Platform A. Throughput is measured in environment steps per second; the reported values are not speedup ratios.
Figure 8: Environment-execution latency of ROSS-T, SEER w/o STPA, and SEER under different entity scales in the Aircraft workload on Platform A.
Figure 9: MARL environment-execution performance for Aircraft: (a–b) single-thread-normalized speedup versus average event time, and (c–d) single-step environment-execution latency versus entity scale. Speedup is computed relative to the corresponding single-thread throughput under the same workload and platform configuration.
Figure 10: End-to-end MARL inference throughput of PCS on Platform B under varying event granularities and entity scales. Throughput is measured in inference steps per second.
Figure 11: Per-thread CPU-time distribution of ROSS-T, ROOT-Sim, and SEER in the evaluated PCS configuration. A more uniform distribution across worker threads indicates better load balance.
ModelTraining SetTest Set
 MAEMSEMAEMSE
LSTM8.9976155.52969.3542161.8647
XGBoost0.55250.64491.28325.1508
TimesURL1.834413.78152.238616.6097
TR+GP2.653518.37203.618731.2561
STPA0.01080.01090.33944.5944
Table 1: Thread-count prediction errors of the evaluated thread-pool selection methods.
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