Adversarial Exploration Strategy for Self-Supervised Imitation Learning

ICLR 2019 • Zhang-Wei Hong • Tsu-Jui Fu • Tzu-Yun Shann • Yi-Hsiang Chang • Chun-Yi Lee

We present an adversarial exploration strategy, a simple yet effective imitation learning scheme that incentivizes exploration of an environment without any extrinsic reward or human demonstration. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other... (read more)

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