ELSA Lab is a research laboratory focusing on Deep Reinforcement Learning, Intelligent Robotics, and Computer Vision. Please visit our website: https://t.co/WyCrpenL99
#ArtificialIntelligence#MachineLearning#DLSM
[ICLR 2022] Denoising Likelihood Score Matching for Condition Score-Based Data Generation
We propose a new denoising likelihood score-matching (DLSM) loss to deal with the score mismatch issue we found in the existing conditional score-based data generation methods.
[AAMAS 2018] A Deep Policy Inference Q-Network for Multi-Agent Systems
We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other.
Advanced detail please visit: https://t.co/6p3fyo917r
Paper Download: https://t.co/t3q6iFENYC
ELSA Lab is a research laboratory focusing on Deep Reinforcement Learning, Intelligent Robotics, and Computer Vision. Please visit our website: https://t.co/WyCrpeFUnh
[CoRL 2019] Adversarial Active Exploration for Inverse Dynamics Model Learning
We presented an adversarial active exploration, which consists of a DRL agent and an inverse dynamics model competing with each other for efficient data collection.
Advanced detail please visit: https://t.co/jInF91KCqp
Paper Download: https://t.co/W1GO8ikhbj
ELSA Lab is a research laboratory focusing on Deep Reinforcement Learning, Intelligent Robotics, and Computer Vision. Please visit our website: https://t.co/WyCrpenL99
[NeurIPS 2018] Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
We presented a diversity-driven exploration strategy, which can be effectively combined with current DRL algorithms through using an additional distance measure term to the loss function.
ELSA Lab is a research laboratory focusing on Deep Reinforcement Learning, Intelligent Robotics, and Computer Vision. Please visit our website: https://t.co/WyCrpenL99
We provided a distributional perspective on value function factorization methods, and introduced a framework, called DFAC, for integrating distributional RL with MARL domains. We achieve State-of-the-art performance on the 5 Super Hard scenarios in the SMAC benchmark.