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Policy Gradient Methods
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning
Solving Zero-Sum Convex Markov Games
Equivalence of stochastic and deterministic policy gradients
Enhanced DACER Algorithm with High Diffusion Efficiency
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment
Policy Testing in Markov Decision Processes
Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs
KIPPO: Koopman-Inspired Proximal Policy Optimization
Self-Evolving Curriculum for LLM Reasoning
Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
Token-Efficient RL for LLM Reasoning
Evolutionary Policy Optimization
Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targets
Ordering-based Conditions for Global Convergence of Policy Gradient Methods
Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch
Residual Policy Gradient: A Reward View of KL-regularized Objective
ROCM: RLHF on consistency models
Convergence Guarantees of Model-free Policy Gradient Methods for LQR with Stochastic Data
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin
A Self-Supervised Reinforcement Learning Approach for Fine-Tuning Large Language Models Using Cross-Attention Signals
Reevaluating Policy Gradient Methods for Imperfect-Information Games
Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods
Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications
Divergence-Augmented Policy Optimization
An Attentive Graph Agent for Topology-Adaptive Cyber Defence
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning
Self-Interested Agents in Collaborative Learning: An Incentivized Adaptive Data-Centric Framework
Reinforcement Learning: An Overview
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings
Solving Rubik's Cube Without Tricky Sampling
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning
Policy Gradient for Robust Markov Decision Processes
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.