Comparison of Reinforcement Learning Activation Functions to Improve the Performance of the Racing Game Learning Agent
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Recently, research has been actively conducted to create artificial intelligence agents that learn games throughreinforcement learning. There are several factors that determine performance when the agent learns a game, butusing any of the activation functions is also an important factor. This paper compares and evaluates whichactivation function gets the best results if the agent learns the game through reinforcement learning in the 2Dracing game environment. We built the agent using a reinforcement learning algorithm and a neural network. We evaluated the activation functions in the network by switching them together. We measured the reward, theoutput of the advantage function, and the output of the loss function while training and testing. As a result ofperformance evaluation, we found out the best activation function for the agent to learn the game. Thedifference between the best and the worst was 35.4%.
Abstract
Recently, research has been actively conducted to create artificial intelligence agents that learn games throughreinforcement learning. There are several factors that determine performance when the agent learns a game, butusing any of the activation functions is also an important factor. This paper compares and evaluates whichactivation function gets the best results if the agent learns the game through reinforcement learning in the 2Dracing game environment. We built the agent using a reinforcement learning algorithm and a neural network. We evaluated the activation functions in the network by switching them together. We measured the reward, theoutput of the advantage function, and the output of the loss function while training and testing. As a result ofperformance evaluation, we found out the best activation function for the agent to learn the game. Thedifference between the best and the worst was 35.4%.
참고문헌 (12)
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