We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent’s policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights.   NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and Dueling agents (entropy reward and epsilon-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.
A deep reinforcement learning agent with parametric noise added to its weights can be used to aid efficient exploration. This paper introduces NoisyNets, neural networks whose parameters are perturbed by a parametric noise function, that obtain substantial performance improvement over baseline deep reinforcement learning algorithms. New exploration method for deep RL by injecting noise into deep networks' weights, with the noise taking various forms