516 citations · 1.1k across the 9 of their papers we have counts for
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cs.LG2017★ 112 cited
Constrained Policy Optimization
Joshua Achiam, David Held, Aviv Tamar +1
For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the rewar…
cs.LG2017★ 99 cited
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Joshua Achiam, Shankar Sastry
Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have bee…