One approach for improving sample efficiency in cooperative multi-agent learning is to decompose overall tasks into sub-tasks that can be assigned to individual agents. We study this problem in the context of reward machines: symbolic tasks that can be formally decomposed into sub-tasks. In order to handle settings without a priori knowledge of the environment, we introduce a framework that can learn the optimal decomposition from model-free interactions with the environment. Our method uses a task-conditioned architecture to simultaneously learn an optimal decomposition and the corresponding agents' policies for each sub-task. In doing so, we remove the need for a human to manually design the optimal decomposition while maintaining the sample-efficiency benefits of improved credit assignment. We provide experimental results in several deep reinforcement learning settings, demonstrating the efficacy of our approach. Our results indicate that our approach succeeds even in environments with codependent agent dynamics, enabling synchronous multi-agent learning not achievable in previous works.

Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many tasks, LTL is insufficient for task specification; LTL-constrained policy optimization, where the goal is to optimize a scalar reward under LTL constraints, is needed. Prior methods for this constrained problem are restricted to finite state spaces. In this work, we present Cycle Experience Replay (CyclER), a reward-shaping approach to this problem that allows continuous state and action spaces and the use of function approximations. CyclER guides a policy towards satisfaction by encouraging partial behaviors compliant with the LTL constraint, using the structure of the constraint. In doing so, it addresses the optimization challenges stemming from the sparse nature of LTL satisfaction. We evaluate CyclER in three continuous control domains. On these tasks, CyclER outperforms existing reward-shaping methods at finding performant and LTL-satisfying policies.

Active learning is a well-studied approach to learning formal specifications, such as automata. In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels. The combination of pair-wise preferences and membership labels allows for a more flexible approach to active specification learning, which previously relied on membership labels only. We instantiate our framework in two different domains, demonstrating the generality of our approach. Our results suggest that learning from both modalities allows us to robustly and conveniently identify specifications via membership and preferences.

If much of the information is irrelevant, it's easy to To optimally coordinate with others in cooperative imagine how this could lead to significant increases in efficiency games, it is often crucial to have information for finding optimal policies. For example, this could about one's collaborators: successful driving requires allow a focused effort on few-shot or zero-shot adaptation to understanding which side of the road to co-players (Zand et al., 2022; Albrecht & Stone, 2017; Stone drive on. However, not every feature of collaborators et al., 2010; Hu et al., 2020) or more efficient DecPOMDP is strategically relevant: the fine-grained planning algorithms (Szer & Charpillet, 2006; Seuken & acceleration of drivers may be ignored while maintaining Zilberstein, 2007). In order to leverage these benefits, we optimal coordination. We show that there build the theory, data structures, and algorithms required to is a well-defined dichotomy between strategically distinguish between relevant and irrelevant information.

Advancements in simulation and formal methods-guided environment sampling have enabled the rigorous evaluation of machine learning models in a number of safety-critical scenarios, such as autonomous driving. Application of these environment sampling techniques towards improving the learned models themselves has yet to be fully exploited. In this work, we introduce a novel method for improving imitation-learned models in a semantically aware fashion by leveraging specification-guided sampling techniques as a means of aggregating expert data in new environments. Specifically, we create a set of formal specifications as a means of partitioning the space of possible environments into semantically similar regions, and identify elements of this partition where our learned imitation behaves most differently from the expert. We then aggregate expert data on environments in these identified regions, leading to more accurate imitation of the expert's behavior semantics. We instantiate our approach in a series of experiments in the CARLA driving simulator, and demonstrate that our approach leads to models that are more accurate than those learned with other environment sampling methods.

This paper considers the problem of learning history dependent task specifications, e.g. automata and temporal logic, from expert demonstrations. Unfortunately, the (countably infinite) number of tasks under consideration combined with an a-priori ignorance of what historical features are needed to encode the demonstrated task makes existing approaches to learning tasks from demonstrations inapplicable. To address this deficit, we propose Demonstration Informed Specification Search (DISS): a family of algorithms parameterized by black box access to (i) a maximum entropy planner and (ii) an algorithm for identifying concepts, e.g., automata, from labeled examples. DISS works by alternating between (i) conjecturing labeled examples to make the demonstrations less surprising and (ii) sampling concepts consistent with the current labeled examples. In the context of tasks described by deterministic finite automata, we provide a concrete implementation of DISS that efficiently combines partial knowledge of the task and a single expert demonstration to identify the full task specification.

We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and interpretability; however, learning them requires optimizing over a combinatorial space of program "architectures". We frame this optimization problem as a search in a weighted graph whose paths encode top-down derivations of program syntax. Our key innovation is to view various classes of neural networks as continuous relaxations over the space of programs, which can then be used to complete any partial program. This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search. We instantiate our approach on top of the A-star algorithm and an iteratively deepened branch-and-bound search, and use these algorithms to learn programmatic classifiers in three sequence classification tasks. Our experiments show that the algorithms outperform state-of-the-art methods for program learning, and that they discover programmatic classifiers that yield natural interpretations and achieve competitive accuracy.