108 results on '"Thomy Phan"'
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2. Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization.
3. ClusterComm: Discrete Communication in Decentralized MARL Using Internal Representation Clustering.
4. Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics Through Multi-Agent Reinforcement Learning Algorithms.
5. Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search.
6. Quantum Circuit Design: A Reinforcement Learning Challenge.
7. Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search.
8. Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic.
9. Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization.
10. MEDIATE: Mutually Endorsed Distributed Incentive Acknowledgment Token Exchange.
11. Adaptive Bi-nonlinear Neural Networks Based on Complex Numbers with Weights Constrained Along the Unit Circle.
12. CROP: Towards Distributional-Shift Robust Reinforcement Learning Using Compact Reshaped Observation Processing.
13. Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability.
14. Attention-Based Recurrency for Multi-Agent Reinforcement Learning under State Uncertainty.
15. Emergent cooperation from mutual acknowledgment exchange in multi-agent reinforcement learning.
16. Capturing Dependencies Within Machine Learning via a Formal Process Model.
17. Towards Anomaly Detection in Reinforcement Learning.
18. Emergent Cooperation from Mutual Acknowledgment Exchange.
19. Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization.
20. DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training.
21. Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search.
22. Challenges for Reinforcement Learning in Quantum Computing.
23. VAST: Value Function Factorization with Variable Agent Sub-Teams.
24. Specification Aware Multi-Agent Reinforcement Learning.
25. SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning.
26. Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition.
27. The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline.
28. Insights on Training Neural Networks for QUBO Tasks.
29. Towards Ecosystem Management from Greedy Reinforcement Learning in a Predator-Prey Setting.
30. Foraging Swarms using Multi-Agent Reinforcement Learning.
31. Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA.
32. Learning and Testing Resilience in Cooperative Multi-Agent Systems.
33. A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games.
34. Uncertainty-based Out-of-Distribution Classification in Deep Reinforcement Learning.
35. Nash Equilibria in Multi-Agent Swarms.
36. Multi-agent Reinforcement Learning for Bargaining under Risk and Asymmetric Information.
37. Productive fitness in diversity-aware evolutionary algorithms.
38. Emergent Escape-based Flocking behavior using Multi-Agent Reinforcement Learning.
39. Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search.
40. Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning.
41. Scenario co-evolution for reinforcement learning on a grid world smart factory domain.
42. Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling.
43. The scenario coevolution paradigm: adaptive quality assurance for adaptive systems.
44. Action Markets in Deep Multi-Agent Reinforcement Learning.
45. Anomaly Detection in Spatial Layer Models of Autonomous Agents.
46. Monitoring Autonomous Agents in Self-Organizing Industrial Systems.
47. The Sharer's Dilemma in Collective Adaptive Systems of Self-interested Agents.
48. Risk-Sensitivity in Simulation Based Online Planning.
49. Leveraging Statistical Multi-Agent Online Planning with Emergent Value Function Approximation.
50. Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms.
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