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Self-Imitation Learning
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This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed to verify our hypothesis that exploiting past good experiences can indirectly drive deep exploration. Our empirical results show that SIL significantly improves advantage actor-critic (A2C) on several hard exploration Atari games and is competitive to the state-of-the-art count-based exploration methods. We also show that SIL improves proximal policy optimization (PPO) on MuJoCo tasks.
Forward citations
Cited by 2 Pith papers
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Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation
SIDP trains a diffusion policy for visual navigation by reward-weighting its own sampled trajectories, improving success rate and cutting inference latency.
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Effective Reinforcement Learning for Reasoning in Language Models
DASH uses large-batch sampling plus filtering of low-advantage examples to cut RL training time for small reasoning models by about 83% with no measured accuracy loss.
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