QED bounds cross-run KL divergence in Boltzmann policies by setting temperature proportional to Q-disagreement and reduces return variance by two orders of magnitude on 18 continuous-control tasks without performance loss.
Proceedings of the 35th International Conference on Machine Learning , pages =
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
FPILOT optimizes pre-trained RL trading policies at inference time using forecasted price trajectories to improve portfolio allocations and risk-adjusted returns on the DJ30 benchmark.
Dual-Window Smoothing uses an execution window for deterministic smoothness and a value window to correct critic bias, plus a first-order temporal regularizer, to achieve smoother RL control than explicit chunking or standard baselines.
citing papers explorer
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Behavior-Consistent Deep Reinforcement Learning
QED bounds cross-run KL divergence in Boltzmann policies by setting temperature proportional to Q-disagreement and reduces return variance by two orders of magnitude on 18 continuous-control tasks without performance loss.
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Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
FPILOT optimizes pre-trained RL trading policies at inference time using forecasted price trajectories to improve portfolio allocations and risk-adjusted returns on the DJ30 benchmark.
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Implicit Action Chunking for Smooth Continuous Control
Dual-Window Smoothing uses an execution window for deterministic smoothness and a value window to correct critic bias, plus a first-order temporal regularizer, to achieve smoother RL control than explicit chunking or standard baselines.