Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: The commonly used Q-learning algorithm combined with function approximation induces systematic overestimations of state-action values. These systematic errors might cause instability, poor performance and sometimes divergence of learning. In this work, we present the AVERAGED TARGET DQN (ADQN) algorithm, an adaptation to the DQN class of algorithms which uses a weighted average over past learned networks to reduce generalization noise variance. As a consequence, this leads to reduced overestimations, more stable learning process and improved performance. Additionally, we analyze ADQN variance reduction along trajectories and demonstrate the performance of ADQN on a toy Gridworld problem, as well as on several of the Atari 2600 games from the Arcade Learning Environment.

Deep Reinforcement Learning with Averaged Target DQN