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Policy Gradient Methods

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Akella17/Deep-Bayesian-Quadrature-Policy-Optimization

공식 코드

Deep Bayesian Quadrature Policy Optimization

https://github.com/Breakend/BayesianPolicyGradients

공식 코드

Bayesian Policy Gradients via Alpha Divergence Dropout Inference

https://github.com/Breakend/ReproducibilityInContinuousPolicyGradientMethods

공식 코드

Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

https://github.com/GRASP-ML/LPG-FTW

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Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting

https://github.com/HTRPOCODES/HTRPO-v2

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Hindsight Trust Region Policy Optimization

https://github.com/Hwhitetooth/lirpg

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On Learning Intrinsic Rewards for Policy Gradient Methods

https://github.com/JerrisonWang/JMLR-DRPMD

공식 코드

Policy Gradient for Robust Markov Decision Processes

https://github.com/Khrylx/Transform2Act

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Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design

https://github.com/KyunghyunLee/aes-rl

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An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search

https://github.com/MathPhysSim/FERMI_RL_Paper

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Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

https://github.com/MatiasAlvo/Neural_inventory_control

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Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization

https://github.com/NikeHop/CSRL

공식 코드

Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods

https://github.com/Scitator/Run-Skeleton-Run

공식 코드

Run, skeleton, run: skeletal model in a physics-based simulation

https://github.com/ShangtongZhang/DeepRL

공식 코드

Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch

https://github.com/Timsey/pg_mri

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Experimental design for MRI by greedy policy search

https://github.com/ZhengyaoJiang/NLRL

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Neural Logic Reinforcement Learning

https://github.com/abrazinskas/selsum

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Learning Opinion Summarizers by Selecting Informative Reviews

https://github.com/adrienbolland/jointly-learning-environments-and-control-policies-with-projected-stochastic-gradient-ascent

공식 코드

Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent

https://github.com/allenai/rl4lms

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Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

https://github.com/artberryx/SAR

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Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods

https://github.com/boschresearch/DD_OPG

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Trajectory-Based Off-Policy Deep Reinforcement Learning

https://github.com/clearoboticslab/learningwithsimplemodels.jl

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Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models

https://github.com/col-tasas/2025-PGforLQRwithStochastic

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Convergence Guarantees of Model-free Policy Gradient Methods for LQR with Stochastic Data

https://github.com/cselab/smarties

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Remember and Forget for Experience Replay

https://github.com/dibyaghosh/dnc

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Divide-and-Conquer Reinforcement Learning

https://github.com/dkangin/baselines

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On-Policy Trust Region Policy Optimisation with Replay Buffers

https://github.com/facebookresearch/WhereDidMyOptimumGo

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Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods

https://github.com/gabrfarina/exp-a-spiel

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Reevaluating Policy Gradient Methods for Imperfect-Information Games

https://github.com/gaosh/MBPG

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Momentum-Based Policy Gradient Methods

https://github.com/gauthamvasan/avg

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Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers

https://github.com/gebob19/natural-policy-gradient-reinforcement-learning

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Understanding the Effects of Second-Order Approximations in Natural Policy Gradient Reinforcement Learning

https://github.com/grimmlab/gumbeldore

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Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement

https://github.com/hsvgbkhgbv/SQDDPG

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Shapley Q-value: A Local Reward Approach to Solve Global Reward Games

https://github.com/illidanlab/rpg

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Ranking Policy Gradient

https://github.com/ilyaorson/cyberdreamcatcher

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An Attentive Graph Agent for Topology-Adaptive Cyber Defence

https://github.com/irom-lab/Invariant-Policy-Optimization

공식 코드

Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning

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