UCB and InfoGain Exploration via Q-Ensembles
Richard Y. Chen OpenAI [email protected] Szymon Sidor OpenAI [email protected] Pieter Abbeel OpenAI University of California, Berkeley [email protected] John Schulman OpenAI [email protected]
We show how an ensemble of Q∗ -functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the Q-learning setting. First we propose an exploration strategy based on upper-confidence bounds (UCB). Next, we define an “InfoGain” exploration bonus, which depends on the disagreement of the Q-ensemble. Our experiments show significant gains on the Atari benchmark.