SAC extends maximum-entropy RL into a stable off-policy actor-critic method with constrained temperature tuning, outperforming prior algorithms in sample efficiency and consistency on locomotion and manipulation tasks.
Deep reinforcement learning that matters
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.
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Soft Actor-Critic is an off-policy maximum-entropy actor-critic algorithm that achieves state-of-the-art performance and high stability on continuous control benchmarks.
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.
RL Developer Memory is a feedback-normalized, safety-gated memory architecture for RL coding agents that logs contextual decisions and applies conservative off-policy gates to maintain 80% decision accuracy and full hard-negative suppression on a 200-case benchmark.
Systematic evaluation of over 100 ML4H papers finds poorer reproducibility than other ML fields, driven by limited data and code access, and offers recommendations to data providers, publishers, and researchers.
AI/ML weather tools face integration challenges from mismatched 'regimes of scale' in how data and models are organized compared to traditional meteorology practices.
citing papers explorer
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Soft Actor-Critic Algorithms and Applications
SAC extends maximum-entropy RL into a stable off-policy actor-critic method with constrained temperature tuning, outperforming prior algorithms in sample efficiency and consistency on locomotion and manipulation tasks.
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Soft Actor-Critic is an off-policy maximum-entropy actor-critic algorithm that achieves state-of-the-art performance and high stability on continuous control benchmarks.
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Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
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Temporal Reasoning Is Not the Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA
Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.
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Feedback-Normalized Developer Memory for Reinforcement-Learning Coding Agents: A Safety-Gated MCP Architecture
RL Developer Memory is a feedback-normalized, safety-gated memory architecture for RL coding agents that logs contextual decisions and applies conservative off-policy gates to maintain 80% decision accuracy and full hard-negative suppression on a 200-case benchmark.
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Reproducibility in Machine Learning for Health
Systematic evaluation of over 100 ML4H papers finds poorer reproducibility than other ML fields, driven by limited data and code access, and offers recommendations to data providers, publishers, and researchers.
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Regimes of Scale in AI Meteorology
AI/ML weather tools face integration challenges from mismatched 'regimes of scale' in how data and models are organized compared to traditional meteorology practices.