Reinforcement Learning Algorithms with Python

Policy gradient algorithms

The other family of MF algorithms is that of the policy gradient methods (or policy optimization methods). They have a more direct and obvious interpretation of the RL problem, as they learn directly from a parametric policy by updating the parameters in the direction of the improvements. It's based on the RL principle that good actions should be encouraged (by boosting the gradient of the policy upward) while discouraging bad actions.

Contrary to value function algorithms, policy optimization mainly requires on-policy data, making these algorithms more sample inefficient. Policy optimization methods can be quite unstable due to the fact that taking the steepest ascent in the presence of surfaces with high curvature ...