1k citations · 2.1k across the 19 of their papers we have counts for
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Emergent Complexity via Multi-Agent Competition
Trapit Bansal, Jakub Pachocki, Szymon Sidor +2
Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the…
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
Ken Kansky, Tom Silver, David A. Mély +7
The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. Nonetheless, progress…
Parameter Space Noise for Exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal +6
Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent'…
UCB Exploration via Q-Ensembles
Richard Y. Chen, Szymon Sidor, Pieter Abbeel +1
We show how an ensemble of -functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit s…