Contents
What is policy gradient in reinforcement learning?
Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent.
Is REINFORCE algorithm on policy?
REINFORCE belongs to a special class of Reinforcement Learning algorithms called Policy Gradient algorithms. A simple implementation of this algorithm would involve creating a Policy: a model that takes a state as input and generates the probability of taking an action as output.
How is reinforce used in a policy gradient?
REINFORCE is a Monte-Carlo variant of policy gradients (Monte-Carlo: taking random samples). The agent collects a trajectory τ of one episode using its current policy, and uses it to update the policy parameter. Since one full trajectory must be completed to construct a sample space, REINFORCE is updated in an off-policy way.
How to reduce variance in a policy gradient?
A common way to reduce variance is subtract a baseline b (s) from the returns in the policy gradient. The baseline is essentially a proxy for the expected actual return, and it mustn’t introduce any bias to the policy gradient. In fact, the value function itself is a good candidate for baseline.
Are there any caveats to a policy gradient method?
The biggest caveat of policy gradient methods is high variance, and this is usually addressed by utilizing the temporal structure (REINFORCE), introducing a baseline, or adding bias (actor-critic). It’s also worthwhile to notice that policy gradient method only guarantee convergence to local maxima.
How are policy gradient algorithms used in RL?
Policy gradient methods are very popular reinforcement learning (RL) algorithms. They are very useful in that they can directly model the policy, and they work in both discrete and continuous space.