May 19, 2025 · Artificial Intelligence

Understanding Policy Evaluation and Improvement in Reinforcement Learning

This article explains how to solve Bellman equations, use iterative policy‑evaluation methods, apply the policy‑improvement theorem, and combine both steps in policy iteration, value iteration, and asynchronous variants, illustrated with a 5‑state example and a 4×4 gridworld.

Bellman equationGridWorldgeneralized policy iteration

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Understanding Policy Evaluation and Improvement in Reinforcement Learning

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Dec 10, 2019 · Artificial Intelligence

Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights

This article details a series of reinforcement‑learning experiments on the 2048 game, from random baselines through DQN implementations, classical value‑iteration methods, network redesigns, and Monte‑Carlo tree search, highlighting challenges such as reward design, over‑estimation, and exploration while achieving scores up to 34 000 and tiles of 2048.

2048AIDQN

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Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights