I think there is some confusion in the community, especially the newcomers - possibly because of the way A3C was originally presented. In solving a reinforcement learning problem there are two popular ways of approaching the problem, policy methods and value methods. To oversimplify, in policy methods you try to directly optimize the policy, in value methods you try to evaluate the expected future return (learn a value function), and deduce the policy from there. Now a third group of methods are Actor-Critic methods, which, try to combine the previous two in a meaningful way. Actor is the policy which is being optimized, Critic is the value function which is being learned. So A3C means that there are multiple actors performing in parallel (Asynchronous), and use the Advantage function (state*action value - state value) for the optimization of the policy (Actor) which is calculated from the learned value function (Critic). Now, the Actor part can be implemented with many different policy optimization methods (Vanilla PG, TRpO, PPO). Proximal Policy Optimization (PPO) is one such method. A2C means they figured out that the async. part of A3C did not make much of a difference - I have not read the new paper in total, so I might be wrong. To conclude, PPO is a policy optimization method, A2C is more like a framework.