How is policy gradient used in reinforcement learning?

How is policy gradient used in reinforcement learning?

The policy gradient (PG) algorithm is a model-free, online, on-policy reinforcement learning method. A PG agent is a policy-based reinforcement learning agent that uses the REINFORCE algorithm to directly compute an optimal policy which maximizes the long-term reward. The action space can be either discrete or continuous.

How to compute the discount factor γ in MATLAB?

To specify the discount factor γ, use the DiscountFactor option. To compute the cumulative reward, the agent first computes a next action by passing the next observation Si’ from the sampled experience to the target actor. The agent finds the cumulative reward by passing the next action to the target critic.

What is the gradient of the actor output?

Update the actor parameters using the following sampled policy gradient to maximize the expected discounted reward. Here, Gai is the gradient of the critic output with respect to the action computed by the actor network, and Gμi is the gradient of the actor output with respect to the actor parameters.

How to create a deep deterministic policy gradient agent?

Create the agent using an rlDDPGAgent object. Alternatively, you can create actor and critic representations and use these representations to create your agent. In this case, ensure that the input and output dimensions of the actor and critic representations match the corresponding action and observation specifications of the environment.

What is Policy Gradient. Policy gradient is an approach to solve reinforcement learning problems. If you haven’t looked into the field of reinforcement learning, please first read the section “A (Long) Peek into Reinforcement Learning » Key Concepts” for the problem definition and key concepts.

What are the key concepts of reinforcement learning?

If you haven’t looked into the field of reinforcement learning, please first read the section “A (Long) Peek into Reinforcement Learning » Key Concepts” for the problem definition and key concepts. Here is a list of notations to help you read through equations in the post easily.

Are there any new algorithms for policy gradient?

Abstract: In this post, we are going to look deep into policy gradient, why it works, and many new policy gradient algorithms proposed in recent years: vanilla policy gradient, actor-critic, off-policy actor-critic, A3C, A2C, DPG, DDPG, D4PG, MADDPG, TRPO, PPO, ACER, ACTKR, SAC, TD3 & SVPG.

https://www.youtube.com/watch?v=vQ_ifavFBkI