Research map / Reinforcement learning / Policy gradient and actor-critic
Self-Imitation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add self-imitation learning to A2C | A2C · 49 Atari games | median human-normalized score: 96.1% -> 138.7% | Helped |
| Add self-imitation learning to A2C | A2C · Hard-exploration Atari games | score | Helped |
| Use A2C+SIL instead of A2C | A2C · Venture | score: 0 -> 0 | No clear effect |
| Add self-imitation learning to A2C | A2C · Key-Door-Treasure | learning speed | Helped |
| Combine self-imitation learning with count-based exploration | A2C · Apple-Key-Door-Treasure | optimal policy learning | Helped |
| Add prioritized experience replay with ACER objective | A2C · 49 Atari games | median human-normalized score: 96.1% -> 46.8% | Hurt |
| Add self-imitation learning to PPO | PPO · MuJoCo: Swimmer, Walker2d, and Ant | reward | Helped |
| Add self-imitation learning to PPO | PPO · Delayed-reward MuJoCo tasks | reward | Helped |
| Reduce the number of SIL updates per iteration or lower the SIL loss weight later in training (ablation) | A2C+SIL · James Bond and Star Gunner | performance | Helped |
Closest papers by meaning
- Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration (ICLR 2025 · similarity 0.88)
- IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic (ICML 2025 · similarity 0.87)
- Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Games (NeurIPS 2024 · similarity 0.86)
- Adversarial Imitation Learning via Boosting (ICLR 2024 · similarity 0.86)
- Hybrid Inverse Reinforcement Learning (ICML 2024 · similarity 0.85)
- Imitation Learning from Observation with Automatic Discount Scheduling (ICLR 2024 · similarity 0.85)
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