Asynchronous Deep Q-Learning for Breakout with RAM inputs
نویسندگان
چکیده
We implemented Asynchronous Deep Q-learning to learn the Atari 2600 game Breakout with RAM inputs. We tested the performance of the our agent by varying network structure, training policy, and environment settings. We saw the he most notable improvement through changing the environment settings. Furthermore, we observed interesting training effects when we used a Boltzmann-Q Policy that encouraged exploration by putting an upper bound on the greediness of the algorithm.
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