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Tzu-Yun Shann

2 accepted papers

2019

Adversarial Active Exploration for Inverse Dynamics Model Learning

CoRL 2019

We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any human intervention. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model c

2018

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning

NeurIPS 2018poster

Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or sparse rewards. To tackle this problem, we present a diversity-driven approach for exploration, which can be easily combined…