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 is crucial for deploying RL agents in real-world applications%2C as it aims to maximize long-term rewards while satisfying safety constraints. However%2C safe RL often suffers from sample inefficiency%2C requiring extensive interactions with the environment to learn a safe policy. We propose Efficient Safe Policy Optimization (ESPO)%2C a novel approach that enhances the efficiency of safe RL through sample manipulation. ESPO employs an optimization framework with three modes: maximizing rewards%2C minimizing costs%2C and balancing the trade-off between the two. By dynamically adjusting the sampling process based on the observed conflict between reward and safety gradients%2C ESPO theoretically guarantees convergence%2C optimization stability%2C and improved sample complexity bounds. Experiments on the Safety-MuJoCo and Omnisafe benchmarks demonstrate that ESPO significantly outperforms existing primal-based and primal-dual-based baselines in terms of reward maximization and constraint satisfaction. Moreover%2C ESPO achieves substantial gains in sample efficiency%2C requiring 25--29%25 fewer samples than baselines%2C and reduces training time by 21--38%25.?quality=80&w=800)
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