Search Results for author:
Found 1 papers, 0 papers with code
Towards Combining On-Off-Policy Methods for Real-World Applications
In this paper, we point out a fundamental property of the objective in reinforcement learning, with which we can reformulate the policy gradient objective into a perceptron-like loss function, removing the need to distinguish between on and off policy training.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
