8 papers
Explainably Safe Reinforcement Learning
Sabine Rieder, Stefan Pranger, Debraj Chakraborty +2
Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly important for learned systems, whos…
Synthesizing POMDP Policies: Sampling Meets Model-checking via Learning
Debraj Chakraborty, Anirban Majumdar, Prince Mathew +2
Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods scale well, they lack formal…
Explainable Representation of Finite-Memory Policies for POMDPs using Decision Trees
Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav +1
Partially Observable Markov Decision Processes (POMDPs) are a fundamental framework for decision-making under uncertainty and partial observability. Since in general optimal polici…
Resilient Strategies for Stochastic Systems: How Much Does It Take to Break a Winning Strategy?
Kush Grover, Markel Zubia, Debraj Chakraborty +3
We study the problem of resilient strategies in the presence of uncertainty. Resilient strategies enable an agent to make decisions that are robust against disturbances. In particu…
Planar Herding of Multiple Evaders with a Single Herder
Rishabh Kumar Singh, Debraj Chakraborty
A planar herding problem is considered, where a superior pursuer herds a flock of non-cooperative, inferior evaders around a predefined target point. An inverse square law of repul…
1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization
Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav +4
Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when…