2 papers
cs.LG2026
Temporally Interpretable Differentiable Decision Trees
Eisuke Hirota, Aarav Sane, Rohan Paleja
Interpretability offers a solution to safe autonomy by providing transparency into an agent's underlying decision-making model. Within sequential-decision making tasks, differentia…
cs.AI2026
Differentiable Belief-based Opponent Shaping
Aarav G Sane, Karthik Sivachandran, Rohan Paleja
Human coordination often relies on the ability to influence the beliefs of others through strategic action. In multi-agent reinforcement learning, opponent shaping attempts to repl…