A DRL-based automated testing framework is shown to distinguish between GA-based and random procedural content generation in a serious game, with GA versions yielding higher agent win rates.
Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks
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Learning agents can make use of Reinforcement Learning (RL) to decide their actions by using a reward function. However, the learning process is greatly influenced by the elect of values of the hyperparameters used in the learning algorithm. This work proposed a Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) based method, which makes use of the Genetic Algorithm (GA) to fine-tune the hyperparameters' values. This method (GA+DDPG+HER) experimented on six robotic manipulation tasks: FetchReach; FetchSlide; FetchPush; FetchPickAndPlace; DoorOpening; and AuboReach. Analysis of these results demonstrated a significant increase in performance and a decrease in learning time. Also, we compare and provide evidence that GA+DDPG+HER is better than the existing methods.
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A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents
A DRL-based automated testing framework is shown to distinguish between GA-based and random procedural content generation in a serious game, with GA versions yielding higher agent win rates.