Reinforcement Learning approach for Real Time Strategy Games Battle city
  and S3
  In this paper we proposed reinforcement learning algorithms with the
generalized reward function. In our proposed method we use Q-learning and SARSA
algorithms with generalised reward function to train the reinforcement learning
agent. We evaluated the performance of our proposed algorithms on two real-time
strategy games called BattleCity and S3. There are two main advantages of
having such an approach as compared to other works in RTS. (1) We can ignore
the concept of a simulator which is often game specific and is usually hard
coded in any type of RTS games (2) our system can learn from interaction with
any opponents and quickly change the strategy according to the opponents and do
not need any human traces as used in previous works. Keywords : Reinforcement
learning, Machine learning, Real time strategy, Artificial intelligence.
