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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/uoe-agents/robust_onpolicy_data_collection

공식 코드

Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning

https://github.com/uoe-agents/seac

공식 코드

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

https://github.com/uoe-agents/seps

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Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

https://github.com/uoe-agents/smaclite

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SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

https://github.com/uoe-agents/ted

공식 코드

Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning

https://github.com/upb-lea/gym-electric-motor

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Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control

https://github.com/uranium11010/rl-skill-theory

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When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions

https://github.com/usail-hkust/llmtscs

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LLMLight: Large Language Models as Traffic Signal Control Agents

https://github.com/usaito/icml2022-mips

공식 코드

Off-Policy Evaluation for Large Action Spaces via Embeddings

https://github.com/usaito/www2024-lope

공식 코드

Long-term Off-Policy Evaluation and Learning

https://github.com/usc-psychsim/mirl-tom-aamas23

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Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning

https://github.com/ushaham/adaptiveFS

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Learning to Ask Medical Questions using Reinforcement Learning

https://github.com/ut-austin-rpl/amago

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AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents

https://github.com/utexas-bwi/scavenger_hunt_api

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A Scavenger Hunt for Service Robots

https://github.com/utiasDSL/gym-pybullet-drones

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Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control

https://github.com/utiasDSL/safe-control-gym

공식 코드

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

https://github.com/uuujf/GapExploration

공식 코드

Gap-Dependent Unsupervised Exploration for Reinforcement Learning

https://github.com/uuujf/morl

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Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning

https://github.com/uw-madison-lee-lab/sft-pg

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Optimizing DDPM Sampling with Shortcut Fine-Tuning

https://github.com/uw-nsl/tinyv

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TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning

https://github.com/uzh-rpg/flightmare

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Flightmare: A Flexible Quadrotor Simulator

https://github.com/uzh-rpg/rl_vo

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Reinforcement Learning Meets Visual Odometry

https://github.com/uzkent/EfficientObjectDetection

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Efficient Object Detection in Large Images using Deep Reinforcement Learning

https://github.com/vadim0x60/cibi

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BF++: a language for general-purpose program synthesis

https://github.com/vadim0x60/cibi-experiments

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Neurogenetic Programming Framework for Explainable Reinforcement Learning

https://github.com/valeoai/LearningByCheating

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End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances

https://github.com/valeoai/rainbow-iqn-apex

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Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

https://github.com/valeoai/v-max

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V-Max: A Reinforcement Learning Framework for Autonomous Driving

https://github.com/valeriechen/ask-your-humans

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Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning

https://github.com/vanderschaarlab/mlforhealthlabpub

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Scalable Bayesian Inverse Reinforcement Learning

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/edm

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Strictly Batch Imitation Learning by Energy-based Distribution Matching

https://github.com/vanderschaarlab/neurallaplacecontrol

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Neural Laplace Control for Continuous-time Delayed Systems

https://github.com/vaskarnathscale/goal-conditioned-rm

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Learning Goal-Conditioned Representations for Language Reward Models

https://github.com/veda-s4dhak/AQIL

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Augmented Q Imitation Learning (AQIL)

https://github.com/veds12/hecogrid

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Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

https://github.com/venktesh22/ExpressLanes_Deep-RL

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Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple Access Locations

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.