Synthetic Monitoring Environments for Reinforcement Learning
This provides a standardized, transparent testbed for RL evaluation, addressing the problem of isolating algorithm failures for researchers, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the lack of precise diagnostic benchmarks in Reinforcement Learning by introducing Synthetic Monitoring Environments (SMEs), an infinite suite of configurable continuous control tasks with known optimal policies, enabling exact calculation of instantaneous regret and systematic evaluation, and demonstrated its utility through ablations of PPO, TD3, and SAC to reveal how environmental properties impact performance.
Reinforcement Learning (RL) lacks benchmarks that enable precise, white-box diagnostics of agent behavior. Current environments often entangle complexity factors and lack ground-truth optimality metrics, making it difficult to isolate why algorithms fail. We introduce Synthetic Monitoring Environments (SMEs), an infinite suite of continuous control tasks. SMEs provide fully configurable task characteristics and known optimal policies. As such, SMEs allow for the exact calculation of instantaneous regret. Their rigorous geometric state space bounds allow for systematic within-distribution (WD) and out-of-distribution (OOD) evaluation. We demonstrate the framework's benefit through multidimensional ablations of PPO, TD3, and SAC, revealing how specific environmental properties - such as action or state space size, reward sparsity and complexity of the optimal policy - impact WD and OOD performance. We thereby show that SMEs offer a standardized, transparent testbed for transitioning RL evaluation from empirical benchmarking toward rigorous scientific analysis.