Exploration-Free Reinforcement Learning with Linear Function Approximation
Luca Civitavecchia, M. Papini
· 1 citation
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Reusing Past Samples in Proximal Policy Optimization: When and How Does It Help?
Among on-policy deep reinforcement learning methods, Proximal Policy Optimization (PPO) has become the de facto standard, due to its consistently strong empirical performance across diverse application domains. However, on-policy methods are inherently sample inefficient: fresh data collected under the current policy i...
Alessandro Montenegro, Riccardo Venturelli, Marco Mussi et al.
· 0 citations