[1903.00374] Model-Based Reinforcement Learning for Atari


A new model-based approach to reinforcement learning encourages agents to make predictions about future states alongside possible rewards. Most RL techniques focus solely on predicting rewards for certain actions. In this technique, models are encouraged to learn the dynamics of the game itself and use its understanding to improve play. Agents learn to reason about good moves in their minds and then apply their learnings to real-world tests, achieving convergence in far fewer iterations.


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