Popular GitHub Repositories for A2c Algorithm

Accepted by AROB 2021. For letting agents in traffic simulation behave more like humans, we propose a unified mechanism for agents learn to decide various accelerations on deep reinforcement learning and generate a traffic flow behaving variously to simulate the real traffic flow.

A model describing how a car learns to control its acceleration by A2C_TD.

Advantage Actor-Critic (A2C) reinforcement learning algorithm to detect emerging trends in tweets. The RL agent learns to optimize actions (post, edit, delete) based on engagement metrics such as likes, retweets, and quotes.

Solving the Atari Breakout environment using Stable Baselines

Personal sandbox project for testing reinforcement learning algorithms.