Programmatically Interpretable Reinforcement Learning. - Abhinav Verma 001; Vijayaraghavan Murali; Rishabh Singh; Pushmeet Kohli; Swarat Chaudhuri (ICML, 2018)

Verifiable and Interpretable Reinforcement Learning through Program Synthesis. - Abhinav Verma 001 (AAAI, 2019)

Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks. - Joshua J. Michalenko; Ameesh Shah; Abhinav Verma 001; Richard G. Baraniuk; Swarat Chaudhuri; Ankit B. Patel (ICLR (Poster), 2019)

Control Regularization for Reduced Variance Reinforcement Learning. - Richard Cheng; Abhinav Verma 001; Gábor Orosz; Swarat Chaudhuri; Yisong Yue; Joel Burdick (ICML, 2019)

Imitation-Projected Programmatic Reinforcement Learning. - Abhinav Verma 001; Hoang Minh Le 002; Yisong Yue; Swarat Chaudhuri (NeurIPS, 2019)

Imitation-Projected Policy Gradient for Programmatic Reinforcement Learning. - Abhinav Verma 001; Hoang Minh Le 002; Yisong Yue; Swarat Chaudhuri (CoRR, 2019)

Neurosymbolic Reinforcement Learning with Formally Verified Exploration. - Greg Anderson 003; Abhinav Verma 001; Isil Dillig; Swarat Chaudhuri (NeurIPS, 2020)

Learning Differentiable Programs with Admissible Neural Heuristics. - Ameesh Shah; Eric Zhan; Jennifer J. Sun; Abhinav Verma 001; Yisong Yue; Swarat Chaudhuri (NeurIPS, 2020)

Eventual Discounting Temporal Logic Counterfactual Experience Replay. - Cameron Voloshin; Abhinav Verma 001; Yisong Yue (ICML, 2023)

Deep Policy Optimization with Temporal Logic Constraints. - Ameesh Shah; Cameron Voloshin; Chenxi Yang; Abhinav Verma 001; Swarat Chaudhuri; Sanjit A. Seshia (CoRR, 2024)

LTL-Constrained Policy Optimization with Cycle Experience Replay. - Ameesh Shah; Cameron Voloshin; Chenxi Yang; Abhinav Verma 001; Swarat Chaudhuri; Sanjit A. Seshia (Trans. Mach. Learn. Res., 2025)