I am mainly focusing on reinforcement learning. Reinforcement learning searches for a policy of near-optimal decisions, by learning from environment interactions autonomously. Despite the fantastic future, reinforcement learning is still in its early infancy. Its potential has not been fully released in real-life tasks yet. Our team is trying in various aspects to improve reinforcement learning, including theoretical foundation, optimization, model structure, experience reuse, abstraction, model building, etc., heading toward sample-efficient methods for large-scale real-world applications....