In Latest Machine Learning Research, A Group at CMU Release a Simple and Efficient Implementation of Recurrent Model-Free Reinforcement Learning (RL) for Future Work to Use as a Baseline for POMDP Algorithms

Most real-world situations involve noise and incomplete information, unlike decision-making algorithms, which often concentrate on simple problems where most information is already available. To solve such issues, complicated algorithms have been developed. However, a straightforward strategy also exists in theory that can be used in primary and complex situations. The canonical issue formulation for Reinforcement […]

The post In Latest Machine Learning Research, A Group at CMU Release a Simple and Efficient Implementation of Recurrent Model-Free Reinforcement Learning (RL) for …

ai paper summary ai shorts algorithms applications artificial intelligence canada carnegie mellon university country deep learning editors pick free future implementation learning machine machine learning reinforcement reinforcement learning release research staff tech news technology university research usa work