Research map / Reinforcement learning / Visual and representation RL
Rainbow: Combining Improvements in Deep Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine six DQN extensions in Rainbow | DQN · Atari 2600 (57 games), no-ops starts | median human-normalized score: 223% vs 164% | Helped |
| Combine six DQN extensions in Rainbow | DQN · Atari 2600 (57 games), human starts | median human-normalized score: 153% vs 128% | Helped |
| Add prioritized replay (ablation) | Rainbow · Atari 2600 (57 games) | median performance | Helped |
| Add multi-step learning (ablation) | Rainbow · Atari 2600 (57 games) | median performance | Helped |
| Add distributional Q-learning (ablation) | Rainbow · Atari 2600 (57 games), after the first 40 million frames | median performance | Helped |
| Add Noisy Nets instead of epsilon-greedy exploration (ablation) | Rainbow · Atari 2600 (57 games) | median performance | Helped |
| Add the dueling network (ablation) | Rainbow · Atari 2600 (57 games) | median performance | No clear effect |
| Add double Q-learning (ablation) | Rainbow · Atari 2600 (57 games) | median performance | No clear effect |
Closest papers by meaning
- Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research (ICML 2021 · similarity 0.94)
- Faster Deep Reinforcement Learning with Slower Online Network (NeurIPS 2022 · similarity 0.91)
- Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC (ICML 2025 · similarity 0.90)
- Convergent and Efficient Deep Q Learning Algorithm (ICLR 2022 · similarity 0.89)
- Confounding Robust Deep Reinforcement Learning: A Causal Approach (NeurIPS 2025 · similarity 0.87)
- Deep Double Q-learning (NeurIPS 2026 · similarity 0.85)
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