Simple random search provides a competitive approach to reinforcement learning

19 Mar 2018Horia ManiaAurelia GuyBenjamin Recht

A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for continuous control problems, matching state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks... (read more)

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