Limiting-Kernel Q(lambda): Bridging Short and Long Horizons

arXiv:2609.27741 · cs.LG, math.OC · Submitted 2026-09-23 · Read on arXiv

cs.LG, math.OC

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Actor — 20× in this paper · explained in 3 episodes
• Q-learning — 16× in this paper · explained in 3 episodes
• Reinforcement Learning — 15× in this paper · explained in 4 episodes
• Trace — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Loss Function — 6× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 4× in this paper · explained in 2 episodes
• Action Space — 3× in this paper · explained in 1 episode
• Actor-Critic Framework — 3× in this paper · explained in 1 episode
• Convergence Guarantees — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Deflated Dynamics Value Iteration — 2× in this paper
• Reward Function — 2× in this paper · explained in 2 episodes
• Sample Complexity — 2× in this paper · explained in 15 episodes
• Actor-Critic Learning — 1× in this paper · explained in 1 episode
• Actor-Critic Model — 1× in this paper · explained in 1 episode
• Generalized Policy Iteration — 1× in this paper
• Normalized Advantage Function — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target
• Understanding the theoretical properties of projected Bellman equation, linear Q-learning, and approximate value iteration
• Proximal Policy Optimization Algorithms

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