Core Concepts
Log density gradient corrects for residual error in policy gradient estimation, improving sample efficiency in reinforcement learning.
Abstract
Policy gradient methods in reinforcement learning are crucial for success.
Residual error in gradient estimation can be significant and impact sample complexity.
Log density gradient method corrects for this error, improving policy gradient estimation.
Proposed method shows promise in reducing sample complexity and outperforming classical policy gradient methods.
Experimental results demonstrate the effectiveness of the log density gradient approach.
Stats
Policy gradient methods are vital for modern reinforcement learning.
Residual error in gradient estimation can impact sample complexity.
Log density gradient corrects for this error, improving policy gradient estimation.
Quotes
"Policy gradient methods are a vital ingredient behind the success of modern reinforcement learning."
"Log density gradient method corrects for this error, improving policy gradient estimation."