This dissertation presents a work in safe RL, where agents must also respect safety constraints using pure-past linear-time temporal logic (PPLTL), and presents how to enforce safety constraints using pure-past linear-time temporal logic (PPLTL).
Abstract
In recent years, there have been several developments combining reinforcement learning (RL) with techniques from theoretical computer science fields such as logic and formal methods. The main goal of these works was to improve training speed and quality, and in some cases also enforce safety constraints. In this dissertation, we present several works that followed this research line. First, we explore research directions concerning reward machines (RMs), an approach proposed to improve training speed and train agents in achieving tasks that require temporally extended behaviours. Given an abstraction of the environment in which the agent acts, we show how we can generate a reward machine from the set of all plans to achieve the task in the abstraction. As the plans come from an abstraction of the environment, the agent still needs to learn how to enact them in order to achieve the task, which is done via RL. Then, we synthesise reward machines in a cooperative multi-agent scenario by using Alternating-time Temporal Logic (ATL) formulas encoding coalition tasks. By model checking the ATL formula, we can obtain a strategy (if there is any) for the coalition to achieve the task, which is then translated to a RM and used to train the agents. We then present an extension of reward machines that endows them with a pushdown stack, obtaining a "pushdown reward machine" (pdRM). As pdRMs are based on pushdown automata, they can encode a strictly larger set of tasks compared to standard RMs, while still enabling more efficient learning compared to other approaches. Finally, we present a work in safe RL, where agents must also respect safety constraints. We present how to enforce safety constraints using pure-past linear-time temporal logic (PPLTL). Each action is associated to a PPLTL formula, and by evaluating the formulas at each timestep we determine which actions the agent can to perform, guaranteeing constraint satisfaction.
This work proposes a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation, and establishes the new semantics to be the most suitable for synthesizing feedback controllers for complex continuous dynamical systems through reinforcement learning.
Nikhil Singh, Indranil Saha· Journal of Artificial Intell...· 0 citations
This work proposes an extension of the ATACOM framework, a state-of-the-art reliable safety layer that can be integrated with existing Reinforcement Learning algorithms to enforce constraints derived from prior knowledge of the system or learned directly from data.
Paolo Magliano, Puze Liu, Jan Peters et al.· arXiv.org· 0 citations
This work employs Inductive Logic Programming (ILP) to extract symbolic representations of RL policies and define a novel set of explainability metrics, including activation rate, feature coverage, syntactic distance and semantic distance, which provide crucial insights for the transfer and generalization of action-specific policies.
In the quantitative finance area, particularly in order execution, reinforcement learning (RL) has shown great promise due to its ability to interact with market environments based on real data. However, traditional RL methods suffer from slow research speed and rely on static market assumptions, which do not consider the impact of the agent's execution action on the environment. To address these, we propose a Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework. The framework utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code. SE-RL could continuously improve the accuracy of LLM-generated RL algorithms through a dual-enhancement kit at both high-level (prompt refinement) and low-level (parameter fine-tuning). Additionally, we use a multi-agent system to simulate dynamic financial markets, accounting for the impact of order executions on market dynamics. To further enhance training in such a dynamic market, we develop a hybrid environment training method that could rebalance each environment's loss weight. Comprehensive experiments on 200 realistic stock datasets demonstrate that our proposed framework outperforms current state-of-the-art baselines. Project page: https://kdd2026-se-rl.github.io/.
Vincent Fu, Xin-Xin Xu, Weichen Xu et al.· Proceedings of the 32nd ACM...· 0 citations
Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution. It has been widely successful in solving long-horizon and complex tasks, where flat-RL algorithms have difficulty in learning. However, while the low-level agent in HRL benefits from dense feedback and abundant trial opportunities, the high-level agent receives sparse, delayed feedback from the environment and its performance depends on the low-level execution capability. In this paper, we study whether subgoal selection by the high-level agent can be performed more strategically, by providing it with dynamics-aware intrinsic motivation. Since motivation based on primitive transition dynamics would require broad coverage of the state-action space, we propose to use coarse dynamics, i.e., environment transitions aggregated over multiple steps at the temporal scale at which the high-level agent operates. This approach stabilizes the high-level policy by learning to minimize the predictive uncertainty associated with the coarse dynamics, and provides a guided structure for navigation. We model the predictive uncertainty by evaluating different dispersion metrics as approximated by a Mixture Density Network (MDN). Empirically, we observe that a dense, dynamics-aware intrinsic reward leads to risk-averse subgoal selection, enabling it to outperform state-of-the-art HRL methods in non-stationary long-horizon environments.
K. Srivastava, Kshitij Jerath· arXiv.org· 0 citations
Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broader state coverage) have shown promising results, but none scores a goal by novelty and reachability jointly: the two signals are traded off by hand, applied in sequence, or one is neglected outright. In this paper, we introduce a reachability-aware goal-selection framework that explicitly integrates these two aspects, and that can be seamlessly incorporated into any off-policy RL algorithm. To this aim, we propose SUccessor-to-Novelty (SUN), an indicator derived from successor value functions to identify goals that are both novel and reachable. We prove that SUN recovers count-based bonuses in the limit, bounds short-horizon hitting probabilities, and provably rejects unreachable goals. We further present an adaptive goal-selection strategy that leverages these properties, and an accurate yet lightweight pseudocount to avoid the overhead of classic methods. We back up all our claims with thorough benchmarks: SUN consistently outperforms state-of-the-art methods in standard and novel environments with unreachable or hard-to-reach states, irreversible transitions, obstacles, mazes, and unbounded spaces.
Wenyan Yang, Arsenii Mustafin, Dominik Baumann et al.· 0 citations
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