In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. We propose a novel first-order policy optimization method, Interior-point Policy Optimization (IPO), which augments the objective with logarithmic barrier functions, inspired by the interior-point method. Our proposed method is easy to implement with performance guarantees and can handle general types of cumulative multi-constraint settings. We conduct extensive evaluations to compare our approach with state-of-the-art baselines. Our algorithm outperforms the baseline algorithms, in terms of reward maximization and constraint satisfaction.

Rechargeable aluminium-ion batteries (AIBs) are considered to be promising alternatives for current lithium-ion batteries (LIBs), since they can offer the possibilities of low cost with high energy-to-price ratios. Unlike in...

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Reinforcement Learning (RL) algorithms have had tremendous success in simulated domains. These algorithms, however, often cannot be directly applied to physical systems, especially in cases where there are constraints to satisfy (e.g. to ensure safety or limit resource consumption). In standard RL, the agent is incentivized to explore any policy with the sole goal of maximizing reward; in the real world, however, ensuring satisfaction of certain constraints in the process is also necessary and essential. In this article, we overview existing approaches addressing constraints in model-free reinforcement learning. We model the problem of learning with constraints as a Constrained Markov Decision Process and consider two main types of constraints: cumulative and instantaneous. We summarize existing approaches and discuss their pros and cons. To evaluate policy performance under constraints, we introduce a set of standard benchmarks and metrics. We also summarize limitations of current methods and present open questions for future research.

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