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Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/HDDLGym/HDDLGym

공식 코드

HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI Gym

https://github.com/HLTCHKUST/sensational_headline

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Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning

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

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Hindsight Trust Region Policy Optimization

https://github.com/Haichao-Zhang/generative-planning

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Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning

https://github.com/Haipeng-Chen/RL4IM-Contingency

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Contingency-Aware Influence Maximization: A Reinforcement Learning Approach

https://github.com/HammerLabML/RBC-Control-SARL

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Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime

https://github.com/HanGuo97/soft-Q-learning-for-text-generation

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Efficient (Soft) Q-Learning for Text Generation with Limited Good Data

https://github.com/Hananel-Hazan/bindsnet

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BindsNET: A machine learning-oriented spiking neural networks library in Python

https://github.com/HaozheJasper/CBRL_KDD22

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ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning

https://github.com/Harry67Hu/CORY

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Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning

https://github.com/Healthy-AI/TreatmentExploration

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Learning to search efficiently for causally near-optimal treatments

https://github.com/HewlettPackard/trust-ml

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Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness

https://github.com/HeyuanMingong/iwies

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Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments

https://github.com/HeyuanMingong/llirl

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Lifelong Incremental Reinforcement Learning with Online Bayesian Inference

https://github.com/Hhhhhhhhhhao/Thin-Structure-Inpainting

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Blind Inpainting of Large-scale Masks of Thin Structures with Adversarial and Reinforcement Learning

https://github.com/Holmeswww/ml-uwac

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Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

https://github.com/Hongshen-Yang/pair-trading-envs

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Reinforcement Learning Pair Trading: A Dynamic Scaling approach

https://github.com/HuTobias/HIGHLIGHTS-LRP

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Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

https://github.com/HumanCompatibleAI/adversarial-policies

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Adversarial Policies: Attacking Deep Reinforcement Learning

https://github.com/HumanCompatibleAI/atari-irl

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Inverse reinforcement learning for video games

https://github.com/HumanCompatibleAI/imitation

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imitation: Clean Imitation Learning Implementations

https://github.com/HumanCompatibleAI/population-irl

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Multi-task Maximum Entropy Inverse Reinforcement Learning

https://github.com/HumanCompatibleAI/rlsp

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Preferences Implicit in the State of the World

https://github.com/Hwhitetooth/random_gvfs

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Learning State Representations from Random Deep Action-conditional Predictions

https://github.com/HxLyn3/MPPVE

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Model-based Reinforcement Learning with Multi-step Plan Value Estimation

https://github.com/IBM/FormalML

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CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in Coq

https://github.com/IBM/TRAIL

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A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving

https://github.com/IBM/commonsense-rl

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Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines

https://github.com/IBM/constrained-rl

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Constrained Exploration and Recovery from Experience Shaping

https://github.com/IBM/context-relevant-pruning-textrl

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Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games

https://github.com/IBM/parl_agents

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Hierarchical Reinforcement Learning with AI Planning Models

https://github.com/IBM/regen

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ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models

https://github.com/IGITUGraz/spore-nest-module

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Embodied Synaptic Plasticity with Online Reinforcement learning

https://github.com/INFERLab/PROF

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Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization

https://github.com/INK-USC/deceive-KG-models

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Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation

https://github.com/IanYangChina/pybullet_multigoal_gym

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An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

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