10 citations · 18 across the 11 of their papers we have counts for
12 papers
Robust and Diverse Multi-Agent Learning via Rational Policy Gradient
Niklas Lauffer, Ameesh Shah, Micah Carroll +3
Adversarial optimization algorithms that explicitly search for flaws in agents' policies have been successfully applied to finding robust and diverse policies in multi-agent settin…
Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning
Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte, Ameesh Shah +2
We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution. In this setting, using automata to rep…
Learning Affordances at Inference-Time for Vision-Language-Action Models
Ameesh Shah, William Chen, Adwait Godbole +3
Solving complex real-world control tasks often takes multiple tries: if we fail at first, we reflect on what went wrong, and change our strategy accordingly to avoid making the sam…
Learning Symbolic Task Decompositions for Multi-Agent Teams
Ameesh Shah, Niklas Lauffer, Thomas Chen +2
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 th…
LTL-Constrained Policy Optimization with Cycle Experience Replay
Ameesh Shah, Cameron Voloshin, Chenxi Yang +3
Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many settings where both satisfaction and optimality…
Who Needs to Know? Minimal Knowledge for Optimal Coordination
Niklas Lauffer, Ameesh Shah, Micah Carroll +2
To optimally coordinate with others in cooperative games, it is often crucial to have information about one's collaborators: successful driving requires understanding which side of…
Learning Formal Specifications from Membership and Preference Queries
Ameesh Shah, Marcell Vazquez-Chanlatte, Sebastian Junges +1
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…
Specification-Guided Data Aggregation for Semantically Aware Imitation Learning
Ameesh Shah, Jonathan DeCastro, John Gideon +3
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,…
Learning Deterministic Finite Automata Decompositions from Examples and Demonstrations
Niklas Lauffer, Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte +2
The identification of a deterministic finite automaton (DFA) from labeled examples is a well-studied problem in the literature; however, prior work focuses on the identification of…
Demonstration Informed Specification Search
Marcell Vazquez-Chanlatte, Ameesh Shah, Gil Lederman +1
This paper considers the problem of learning temporal task specifications, e.g. automata and temporal logic, from expert demonstrations. Task specifications are a class of sparse m…
Learning Differentiable Programs with Admissible Neural Heuristics
Ameesh Shah, Eric Zhan, Jennifer J. Sun +3
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…
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
Joshua J. Michalenko, Ameesh Shah, Abhinav Verma +3
We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positi…