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.