Double A3C: Deep Reinforcement Learning on OpenAI Gym Games
This work addresses the problem of improving reinforcement learning performance for playing Atari games, but it appears incremental as it builds on existing methods.
The authors tackled the challenge of building efficient neural networks for reinforcement learning in large state spaces by proposing Double A3C, an improved algorithm combining Double Q-learning and A3C, and implemented it to play OpenAI Gym Atari 2600 games, aiming to beat benchmarks.
Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards. Unlike classical Markov Decision Process (MDP) in which agent has full knowledge of its state, rewards, and transitional probability, reinforcement learning utilizes exploration and exploitation for the model uncertainty. Under the condition that the model usually has a large state space, a neural network (NN) can be used to correlate its input state to its output actions to maximize the agent's rewards. However, building and training an efficient neural network is challenging. Inspired by Double Q-learning and Asynchronous Advantage Actor-Critic (A3C) algorithm, we will propose and implement an improved version of Double A3C algorithm which utilizing the strength of both algorithms to play OpenAI Gym Atari 2600 games to beat its benchmarks for our project.