Papers › MicroRacer: a didactic environment for Deep Reinforcement Learning

MicroRacer: a didactic environment for Deep Reinforcement Learning

20 Mar 2022arXiv:2203.10494archive 2025-07-28

Andrea Asperti, Marco Del Brutto

MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many different methods, networks and hyperparameters settings without requiring sophisticated software or the need of exceedingly long training times. Baseline agents for major learning algorithms such as DDPG, PPO, SAC, TD2 and DSAC are provided too, along with a preliminary comparison in terms of training time and performance.

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Tasks

Car Racing Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Methods

1x1 Convolution Adam Average Pooling Batch Normalization Convolution DDPG Dense Connections Dilated Convolution Entropy Regularization Experience Replay Global Average Pooling PPO ReLU SAC Weight Decay

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