Authors :
T Manoj; J Rohit; A Sai Sashank; Asst. Prof. BJ Praveena
Volume/Issue :
Volume 6 - 2021, Issue 4 - April
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3vBuxXZ
Abstract :
In reinforcement learning, the traditional Q
Learning method solves the game by iterating over the
full set of states. Using the Q-Table to implement QLearning is fine in small discrete environments. However
often we realize that we have too many states to track.
An example is Atari games, that can have a large variety
of different screens, and in this case, the problem cannot
be solved with a Q-table. This paper uses a deep neural
network instead of a Q-table to solve it. Atari games are
displayed at a resolution of 210 by 160 pixels, with 128
possible colors for each pixel. This is still technically a
discrete state space but very large to process. To reduce
this complexity, we performed some minimal image
preprocessing. Finally, from the experimental results, it
is concluded that DQN can make the agent get high
scores from Atari game, and experience replay can
make the model training better.
Keywords :
Deep Reinforcement Learning; Artificial Intelligence; Image Processing
In reinforcement learning, the traditional Q
Learning method solves the game by iterating over the
full set of states. Using the Q-Table to implement QLearning is fine in small discrete environments. However
often we realize that we have too many states to track.
An example is Atari games, that can have a large variety
of different screens, and in this case, the problem cannot
be solved with a Q-table. This paper uses a deep neural
network instead of a Q-table to solve it. Atari games are
displayed at a resolution of 210 by 160 pixels, with 128
possible colors for each pixel. This is still technically a
discrete state space but very large to process. To reduce
this complexity, we performed some minimal image
preprocessing. Finally, from the experimental results, it
is concluded that DQN can make the agent get high
scores from Atari game, and experience replay can
make the model training better.
Keywords :
Deep Reinforcement Learning; Artificial Intelligence; Image Processing
