Search Results for author:
Found 2 papers, 1 papers with code
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning
One of the major dilemmas in Reinforcement Learning (RL) where an autonomous agent has to balance two contrasting needs in making its decisions is: exploiting the current knowledge of the environment to maximize the cumulative reward as well as exploring actions that allow improving the knowledge of the environment, hopefully leading to higher reward values (exploration-exploitation trade-off).
Bayesian Optimization and Deep Learning forsteering wheel angle prediction
Automated driving systems (ADS) have undergone a significant improvement in the last years.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
