Rainbow: Combining Improvements in Deep Reinforcement Learning
reinforcement-learningdqnatarideep-learning
Abstraction: Empirical combination of six DQN extensions achieving state-of-the-art Atari performance
Key points:
- Hessel et al. (DeepMind, 2017): examines six independent extensions to DQN and studies their combined effect
- Combined Rainbow agent achieves state-of-the-art on Atari 2600 benchmark in both data efficiency and final performance
- Ablation study quantifies each component's individual contribution to overall performance
- Extensions studied include: double DQN, dueling networks, prioritized replay, multi-step returns, distributional RL, and noisy nets
- Paper motivates systematic combination of complementary improvements rather than isolated evaluations
Connections: Deepmind · Reinforcement Learning · Deep Learning · Dqn
Source: https://arxiv.org/abs/1710.02298