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Data Party THU
Data Party THU
Oct 21, 2025 · Artificial Intelligence

Why DQN Overestimates Q‑Values and How Double DQN Fixes It

The article explains how DQN’s use of the max operator introduces a maximization bias that leads to overestimated Q‑values, and shows how Double DQN separates action selection from value evaluation to eliminate this bias, improving stability and performance in Atari benchmarks.

DQNDouble DQNalgorithm analysis
0 likes · 7 min read
Why DQN Overestimates Q‑Values and How Double DQN Fixes It
AI Algorithm Path
AI Algorithm Path
May 23, 2025 · Artificial Intelligence

Understanding Temporal‑Difference Algorithms in Reinforcement Learning

This tutorial explains temporal‑difference (TD) learning, compares it with dynamic programming and Monte‑Carlo methods, walks through concrete soccer‑match examples, shows one‑step TD versus constant‑α Monte‑Carlo updates, discusses convergence, bias, and introduces popular TD variants such as Sarsa, Q‑learning, Expected Sarsa and double learning.

Monte CarloTD learningmaximization bias
0 likes · 18 min read
Understanding Temporal‑Difference Algorithms in Reinforcement Learning