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1. Power and accountability in reinforcement learning applications to environmental policy

   Chapman, Melissa; Scoville, Caleb; Lapeyrolerie, Marcus; Boettiger, Carl (January 2021, Conference Proceedings on Neural Information Processing Systems, 2021)

   Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We leverage examples from RL applications to climate change mitigation and fisheries management to explore how RL technologies shift the distribution of power between resource users, governing bodies, and private industry. 

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2. Multi-Agent Reinforcement Learning for Alternating-Time Logic

   Hahna, Ernst Moritz; Perez, Mateo; Schewe, Sven; Somenzi, Fabio; Trivedi, Ashutosh; Wojtczak, Dominik (October 2024, Open Access by IOS Press)

   Endriss, Ulle; Melo, Francisco (Ed.)

   Alternating-time temporal logic (ATL) extends branching time logic by enabling quantification over paths that result from the strategic choices made by multiple agents in various coalitions within the system. While classical temporal logics express properties of “closed” systems, ATL can express properties of “open” systems resulting from interactions among several agents. Reinforcement learning (RL) is a sampling-based approach to decision-making where learning agents, guided by a scalar reward function, discover optimal policies through repeated interactions with the environment. The challenge of translating high-level objectives into scalar rewards for RL has garnered increased interest, particularly following the success of model-free RL algorithms. This paper presents an approach for deploying model-free RL to verify multi-agent systems against ATL specifications. The key contribution of this paper is a verification procedure for model-free RL of quantitative and non-nested classic ATL properties, based on Q-learning, demonstrated on a natural subclass of non-nested ATL formulas. 

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3. AWARE: Automate Workload Autoscaling with Reinforcement Learning in Production Cloud Systems

   Qiu, Haoran; Mao, Weichao; Wang, Chen; Franke, Hubertus; Yousseff, Alaa; Kalbarczyk, Zbigniew T.; Iyer, Ravishankar K (July 2023, 2023 USENIX Annual Technical Conference (USENIX ATC 23))

   Workload autoscaling is widely used in public and private cloud systems to maintain stable service performance and save resources. However, it remains challenging to set the optimal resource limits and dynamically scale each workload at runtime. Reinforcement learning (RL) has recently been proposed and applied in various systems tasks, including resource management. In this paper, we first characterize the state-of-the-art RL approaches for workload autoscaling in a public cloud and point out that there is still a large gap in taking the RL advances to production systems. We then propose AWARE, an extensible framework for deploying and managing RL-based agents in production systems. AWARE leverages meta-learning and bootstrapping to (a) automatically and quickly adapt to different workloads, and (b) provide safe and robust RL exploration. AWARE provides a common OpenAI Gym-like RL interface to agent developers for easy integration with different systems tasks. We illustrate the use of AWARE in the case of workload autoscaling. Our experiments show that AWARE adapts a learned autoscaling policy to new workloads 5.5x faster than the existing transfer-learning-based approach and provides stable online policy-serving performance with less than 3.6% reward degradation. With bootstrapping, AWARE helps achieve 47.5% and 39.2% higher CPU and memory utilization while reducing SLO violations by a factor of 16.9x during policy training. 

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4. Deep Reinforcement Learning Verification: A Survey

   https://doi.org/10.1145/3596444  

   Landers, Matthew; Doryab, Afsaneh (December 2023, ACM Computing Surveys)

   Deep reinforcement learning (DRL) has proven capable of superhuman performance on many complex tasks. To achieve this success, DRL algorithms train a decision-making agent to select the actions that maximize some long-term performance measure. In many consequential real-world domains, however, optimal performance is not enough to justify an algorithm’s use—for example, sometimes a system’s robustness, stability, or safety must be rigorously ensured. Thus, methods for verifying DRL systems have emerged. These algorithms can guarantee a system’s properties over an infinite set of inputs, but the task is not trivial. DRL relies on deep neural networks (DNNs). DNNs are often referred to as “black boxes” because examining their respective structures does not elucidate their decision-making processes. Moreover, the sequential nature of the problems DRL is used to solve promotes significant scalability challenges. Finally, because DRL environments are often stochastic, verification methods must account for probabilistic behavior. To address these complications, a new subfield has emerged. In this survey, we establish the foundations of DRL and DRL verification, define a taxonomy for DRL verification methods, describe approaches for dealing with stochasticity, characterize considerations related to writing specifications, enumerate common testing tasks/environments, and detail opportunities for future research. 

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5. Hindsight learning for MDPs with exogenous inputs

   Sinclair, Sean_R; Frujeri, Felipe; Cheng, Ching-An; Marshall, Luke; Barbalho, Hugo; Li, Jingling; Neville, Jennifer; Menache, Ishai; Swaminathan, Adith (July 2023, Proceedings of Machine Learning Research)

   Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker. We model these problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and design a class of data-efficient algorithms for them termed Hindsight Learning (HL). Our HL algorithms achieve data efficiency by leveraging a key insight: having samples of the exogenous variables, past decisions can be revisited in hindsight to infer counterfactual consequences that can accelerate policy improvements. We compare HL against classic baselines in the multi-secretary and airline revenue management problems. We also scale our algorithms to a business-critical cloud resource management problem – allocating Virtual Machines (VMs) to physical machines, and simulate their performance with real datasets from a large public cloud provider. We find that HL algorithms outperform domain-specific heuristics, as well as state-of-the-art reinforcement learning methods. 

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