Hugging Face Trending Papers
Q($γ_H$): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning
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arXiv Machine Learning
Oct 16
Q($\gamma_H$): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning
Q($\gamma_H$) introduces a multi-horizon Q-learning framework that keeps separate discount-specific heads and blends them during control using an entropy-derived confidence signal. The adaptive mixing shifts from short to long horizons as policy confidence grows, allowing the agent to adjust its planning horizon without altering individual Bellman updates. Experiments on classical control and deep reinforcement‑learning tasks show the method excels on delayed‑credit problems compared to fixed‑discount baselines.
arXiv AI
Jul 24
Adaptive Multi-Horizon Reinforcement Learning
arXiv:2607. 20656v1 Announce Type: cross Abstract: Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences.
arXiv AI
Oct 15
AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning
arXiv:2605.06149v2 Announce Type: replace-cross Abstract: The discount factor in reinforcement learning controls both the effective planning horizon and the strength of bootstrapping, yet most deep R...
arXiv AI
Aug 3
Gated Q-learning: Add Off-Policy Bias to Taste
arXiv:2607. 28916v1 Announce Type: cross Abstract: Multistep credit assignment is critical for sample-efficient reinforcement learning, yet managing off-policy bias in Q-learning remains a fundamental challenge.
arXiv Machine Learning
Oct 8
An Informational Curse of Horizon in Goal-Conditioned Policy Learning
The paper identifies an informational curse of horizon in goal-conditioned policy learning, showing that longer goal relabeling horizons during training can hurt policy generalization and performance. Controlled experiments with oracle planners separate training horizons from test horizons, revealing that behavioral cloning policies degrade with longer horizons while reinforcement learning mitigates this effect. The authors attribute this to reduced conditional mutual information between actions and hindsight-relabeled goals, and demonstrate that distilling short-horizon policy Jacobians into long-horizon policies improves performance, especially in combinatorial manipulation tasks.
Hugging Face Trending Papers
Oct 15
V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the action-value function into a state-dependent value co...