Policy Gradient Methods

A2C, or Advantage Actor Critic, is a synchronous version of the A3C policy gradient method. As an alternative to the asynchronous implementation of A3C, A2C is a synchronous, deterministic implementation that waits for each actor to finish its segment of experience before updating, averaging over all of the actors. This more effectively uses GPUs due to larger batch sizes.

Image Credit: OpenAI Baselines

Source: Asynchronous Methods for Deep Reinforcement Learning

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Reinforcement Learning (RL) 52 22.32%
Reinforcement Learning 49 21.03%
Deep Reinforcement Learning 30 12.88%
Decision Making 12 5.15%
Atari Games 10 4.29%
OpenAI Gym 5 2.15%
Continuous Control 5 2.15%
Benchmarking 4 1.72%
Management 4 1.72%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories