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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

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/PKU-AI-Edge/DGN/

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Graph Convolutional Reinforcement Learning

https://github.com/PKU-RL/CLIP4MC

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Reinforcement Learning Friendly Vision-Language Model for Minecraft

https://github.com/PPSantos/hybrid-marl

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Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning

https://github.com/PSVL/DoorGym

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DoorGym: A Scalable Door Opening Environment And Baseline Agent

https://github.com/PTF-transfer/Code_PTF

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Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

https://github.com/PaLeroy/QVMix

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QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning

https://github.com/PaddlePaddle/Paddle

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HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

https://github.com/PaddlePaddle/PaddleRobotics

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Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion

https://github.com/PaddlePaddle/XWorld

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Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents

https://github.com/PaladinEE15/RSAC

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Robust Reinforcement Learning under model misspecification

https://github.com/PartnershipOnAI/safelife

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SafeLife 1.0: Exploring Side Effects in Complex Environments

https://github.com/PatrykChrabaszcz/Canonical_ES_Atari

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Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari

https://github.com/PaulEcoffet/RLCoopExp

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Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate with

https://github.com/PaulsonLab/CAGES

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CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization

https://github.com/Pearl-UTexas/ActiveVaR

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Risk-Aware Active Inverse Reinforcement Learning

https://github.com/PettingZoo-Team/SuperSuit

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SuperSuit: Simple Microwrappers for Reinforcement Learning Environments

https://github.com/Philip-Bachman/ICML-2015

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Variational Generative Stochastic Networks with Collaborative Shaping

https://github.com/PierreExeter/WidowX-reacher

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Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks

https://github.com/PierreExeter/rl_reach

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rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

https://github.com/PierreMarza/dynamic_implicit_representations

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Multi-Object Navigation with dynamically learned neural implicit representations

https://github.com/PierreMarza/teaching_agents_how_to_map

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Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation

https://github.com/PrajwalKoirala/LSPC-Safe-Offline-RL

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Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning

https://github.com/PrajwalKoirala/Offline-Reinforcement-Learning-with-Decision-Tree-Regression

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Solving Offline Reinforcement Learning with Decision Tree Regression

https://github.com/ProSeCo-Planning/ros_proseco_planning/tree/main/python/proseco/inverse_reinforcement_learning

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Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search

https://github.com/QData/dmc_remastered

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Measuring Visual Generalization in Continuous Control from Pixels

https://github.com/QingyuanWuNothing/DFBT

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Directly Forecasting Belief for Reinforcement Learning with Delays

https://github.com/QinwenLuo/OCR-CFT

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Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL

https://github.com/Quiltomics/NLERL

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Modeling natural language emergence with integral transform theory and reinforcement learning

https://github.com/R-Sweke/DeepQ-Decoding

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Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

https://github.com/RC-Dynamics/Coach-RL

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An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer

https://github.com/RL-MLDM/alphagen

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Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

https://github.com/RL-WFU/Drone_field

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Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments

https://github.com/RLE-Foundation/rllte

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RLLTE: Long-Term Evolution Project of Reinforcement Learning

https://github.com/RLGC-Project/RLGC

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Adaptive Power System Emergency Control using Deep Reinforcement Learning

https://github.com/RLforlife/VER

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Revisiting Prioritized Experience Replay: A Value Perspective

https://github.com/Rabbytr/g2p-tsc

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Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control

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