Learning agents with prioritization and parameter noise in continuous state and action space

ICLR 2019 Rajesh DevaraddiG. Srinivasaraghavan

Reinforcement Learning (RL) problem can be solved in two different ways - the Value function-based approach and the policy optimization-based approach - to eventually arrive at an optimal policy for the given environment. One of the recent breakthroughs in reinforcement learning is the use of deep neural networks as function approximators to approximate the value function or q-function in a reinforcement learning scheme... (read more)

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