arXiv:2203.10494
공식 코드 · Papers with Code
MicroRacer: a didactic environment for Deep Reinforcement Learning
원문 초록
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.
함께 살펴볼 논문
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Deep Reinforcement Learning with Gradient Eligibility Traces
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints
Task Independent Capsule-Based Agents for Deep Q-Learning
A State Representation Dueling Network for Deep Reinforcement Learning
Drafting in Collectible Card Games via Reinforcement Learning
토론
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