Noisy Networks for Exploration

Code

• github.com/opendilab/DI-engine
  pytorch★ 3,606
• github.com/Kaixhin/NoisyNet-A3C
  pytorch★ 0
• github.com/LilTwo/DRL-using-PyTorch
  pytorch★ 0
• github.com/YanSong97/Master-thesis
  pytorch★ 0
• github.com/BY571/DQN-Atari-Agents
  pytorch★ 0
• github.com/behzadanksu/rl-attack
  tf★ 0
• github.com/mindspore-courses/Rainbow-MindSpore
  mindspore★ 0
• github.com/Curt-Park/rainbow-is-all-you-need
  none★ 0
• github.com/thomashirtz/noisy-networks
  pytorch★ 0
• github.com/hw9603/DQfD-PyTorch
  pytorch★ 0

Abstract

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 -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.

Tasks

Benchmark Results

Reproductions