Contents
- 1 What do you need to know about policy gradients?
- 2 How are policy and trajectory related in RL problem?
- 3 What are the disadvantages of policy gradients in reinforcement learning?
- 4 What is the derivative of the expected reward?
- 5 What causes a sub optimal shift in the policy distribution?
- 6 How is the vanilla policy gradient algorithm trained?
What do you need to know about policy gradients?
Policy Gradients in a Nutshell. Everything you need to know to get… | by Sanyam Kapoor | Towards Data Science This article aims to provide a concise yet comprehensive introduction to one of the most important class of control algorithms in Reinforcement Learning — Policy Gradients.
This policy is what the agent controls. When an agent follows a policy π, it generates the sequence of states, actions and rewards called the trajectory. Policy: A policy is defined as the probability distribution of actions given a state With all these definitions in mind, let us see how the RL problem looks like formally.
How are policy gradients defined in machine learning?
Like any Machine Learning setup, we define a set of parameters θ (e.g. the coefficients of a complex polynomial or the weights and biases of units in a neural network) to parametrize this policy — π_θ (also written a π for brevity). If we represent the total reward for a given trajectory τ as r ( τ ), we arrive at the following definition.
What are the disadvantages of policy gradients in reinforcement learning?
This is not to say that value based approaches are useless, one of the biggest disadvantages of policy gradients is their high variance estimates of the gradient updates. This leads to very noisy gradient estimates and can de-stabilize the learning process.
What is the derivative of the expected reward?
The Policy Gradient Theorem: The derivative of the expected reward is the expectation of the product of the reward and gradient of the log of the policy π_θ . Now, let us expand the definition of π_θ ( τ ). To understand this computation, let us break it down — P represents the ergodic distribution of starting in some state s_ 0.
Are there any optimal policies in finite MDPs?
All finite MDPs have at least one optimal policy (which can give the maximum reward) and among all the optimal policies at least one is stationary and deterministic. Like any other Machine Learning problem, if we can find the parameters θ ⋆ which maximize J, we will have solved the task.
What causes a sub optimal shift in the policy distribution?
In our context, any erratic trajectory can cause a sub-optimal shift in the policy distribution. This problem is aggravated by the scale of rewards. Consequently, we instead try to optimize for the difference in rewards by introducing another variable called baseline b.
How is the vanilla policy gradient algorithm trained?
Defining a custom loss function and applying the GradientTape functionality, the actor network can be trained using only a few lines of code. At the root of all the sophisticated actor-critic algorithms that are designed and applied these days is the vanilla policy gradient algorithm, which essentially is an actor-only algorithm.
Which is an example of a policy gradient algorithm?
At the root of all the sophisticated actor-critic algorithms that are designed and applied these days is the vanilla policy gradient algorithm, which essentially is an actor-only algorithm. Nowadays, the actor that learns the decision-making policy is often represented by a neural network.