4 papers
QuantFPFlow: Quantum Amplitude Estimation for Fokker--Planck Policy Optimisation in Continuous Reinforcement Learning
Abraham Itzhak Weinberg
We introduce \textbf{QuantFPFlow}, a reinforcement learning framework that integrates quantum amplitude estimation into the Fokker--Planck~(FP) formulation of stochastic policy opt…
Quantum Decision Transformers (QDT): Synergistic Entanglement and Interference for Offline Reinforcement Learning
Abraham Itzhak Weinberg
Offline reinforcement learning enables policy learning from pre-collected datasets without environment interaction, but existing Decision Transformer (DT) architectures struggle wi…
Trust-Based Social Learning for Communication (TSLEC) Protocol Evolution in Multi-Agent Reinforcement Learning
Abraham Itzhak Weinberg
Emergent communication in multi-agent systems typically occurs through independent learning, resulting in slow convergence and potentially suboptimal protocols. We introduce TSLEC…
MACIE: Multi-Agent Causal Intelligence Explainer for Collective Behavior Understanding
Abraham Itzhak Weinberg
As Multi Agent Reinforcement Learning systems are used in safety critical applications. Understanding why agents make decisions and how they achieve collective behavior is crucial.…