- 1 Is A3C a policy?
- 2 What is policy based learning?
- 3 What is A3C in machine learning?
- 4 What is the difference between on-policy and off-policy?
- 5 How is the A3C algorithm used in policy gradients?
- 6 Is there a recurrent version of the A3C algorithm?
- 7 Why is A3C the best algorithm for reinforcement learning?
Is A3C a policy?
1 Answer. A3C is an actor-critic method, which tend to be on-policy (A3C itself is too), because the actor gradient is still computed with an expectation over trajectories sampled from that same policy. TRPO and PPO are both on-policy.
What is policy based learning?
In policy-based methods, instead of learning a value function that tells us what is the expected sum of rewards given a state and an action, we learn directly the policy function that maps state to action (select actions without using a value function).
What is A3C in machine learning?
The Asynchronous Advantage Actor Critic (A3C) algorithm is one of the newest algorithms to be developed under the field of Deep Reinforcement Learning Algorithms. This agents interact with their respective environments Asynchronously, learning with each interaction. Each agent is controlled by a global network.
What is the difference between on-policy and off-policy?
On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data.
How is the A3C algorithm used in policy gradients?
As I will soon explain in more detail, the A3C algorithm can be essentially described as using policy gradients with a function approximator, where the function approximator is a deep neural network and the authors use a clever method to try and ensure the agent explores the state space well.
Is there a recurrent version of the A3C algorithm?
In the paper there are actually two versions of the A3C algorithm: one just uses a feedforward convolutional neural network, while the other includes a recurrent layer. I’ll focus on the first one, in order to simplify everything as much as possible. I also only focus on the discrete action case here.
Why is A3C the best algorithm for reinforcement learning?
I also implemented one step Q-learning and got this to work on Space Invaders, but the reason I focus on A3C is because it is the best performing algorithm from the paper. The exciting thing about the paper, at least for me, is that you don’t need to rely on a GPU for speed.