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cs.LG2026
Synthetic Monitoring Environments for Reinforcement Learning
Leonard Pleiss, Carolin Schmidt, Maximilian Schiffer
Reinforcement Learning (RL) lacks benchmarks that enable precise, white-box diagnostics of agent behavior. Current environments often entangle complexity factors and lack ground-tr…
cs.LG2026
Target-Aligned Reinforcement Learning
Leonard S. Pleiss, James Harrison, Maximilian Schiffer
Many value-based deep reinforcement learning algorithms rely on target networks - lagged copies of the online network - to stabilize training. While effective, this mechanism intro…
cs.LG2025
Reliability-Adjusted Prioritized Experience Replay
Leonard S. Pleiss, Tobias Sutter, Maximilian Schiffer
Experience replay enables data-efficient learning from past experiences in online reinforcement learning agents. Traditionally, experiences were sampled uniformly from a replay buf…