Decentralized Distributed Proximal Policy Optimization

What is DD-PPO?

Decentralized Distributed Proximal Policy Optimization, commonly referred to as DD-PPO, is a method for distributed reinforcement learning in resource-intensive simulated environments. It is a policy gradient method for reinforcement learning that can be used with synchronous distribution. It is a distributed mechanism that has the potential to scale very well therefore making implementations very simple.

Proximal Policy Optimization (PPO)

Proximal Policy Optimization or PPO is a policy gradient method for reinforcement learning. The idea behind PPO is to have an algorithm with the data efficiency and reliable performance of TRPO while using only first-order optimization.

How is DD-PPO Implemented?

DD-PPO implements the following at step K:

  • Worker N has a copy of the parameters Theta^k_n
  • Calculates Gradient, delta theta^k_n
  • Updates Theta via formula: Theta^{k+1}_n = ParamUpdate ( Theta^{k}_n, AllReduce(delta theta^k_1,...., delta theta^k_N)

ParamUpdate is any first-order optimization technique (e.g.gradient descent) and AllReduce performs a reduction over all copies of a variable and returns the result to all workers. Distributed DataParallel scales very well and is reasonably simple to implement with all workers synchronously running identical code.

Why is DD-PPO Important?

DD-PPO is important as it can be used with a synchronous distribution which allows it to be a distributed mechanism that has the potential to scale very well. With DD-PPO, the computation is never stale therefore making it reliable and less expensive. DD-PPO provides larger compute resources which in turn provides better results for simulated environments

DD-PPO is a useful technique for distributed reinforcement learning in resource-intensive simulated environments. It is distributed, decentralized, and synchronous therefore making it a less expensive and more reliable option for reinforcement learning. As we continue to see a need for more complex algorithms, DD-PPO provides a valuable solution to help scale and manage such complex algorithms.

Great! Next, complete checkout for full access to SERP AI.
Welcome back! You've successfully signed in.
You've successfully subscribed to SERP AI.
Success! Your account is fully activated, you now have access to all content.
Success! Your billing info has been updated.
Your billing was not updated.