Papers with Code 아카이브
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/archanabura/dope-doublyoptimisticpessimisticexploration
공식 코드DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
https://github.com/architsharma97/dpo-rlaif
공식 코드A Critical Evaluation of AI Feedback for Aligning Large Language Models
https://github.com/architsharma97/earl_benchmark
공식 코드Autonomous Reinforcement Learning: Formalism and Benchmarking
https://github.com/architsharma97/medal
공식 코드A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
https://github.com/arclab-mit/kspdg
공식 코드Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program
https://github.com/argearriojas/2023-entregrl
공식 코드Entropy Regularized Reinforcement Learning Using Large Deviation Theory
https://github.com/argearriojas/maxent-rl-mdp-scripts
공식 코드Entropy Regularized Reinforcement Learning Using Large Deviation Theory
https://github.com/arjunbhorkar/ReViND
공식 코드https://github.com/arlo-lib/arlo
공식 코드https://github.com/arnab39/EquivariantDQN
공식 코드https://github.com/arpit15/skill-based-exploration-drl
공식 코드Model Learning for Look-ahead Exploration in Continuous Control
https://github.com/arpita8/Estimation-of-Warfarin-Dosage
공식 코드https://github.com/arplaboratory/learning-to-fly
공식 코드https://github.com/arrival-ltd/catalyst-rl-tutorial
공식 코드https://github.com/ars22/scaling-LLM-math-synthetic-data
공식 코드RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
https://github.com/artberryx/pma
공식 코드https://github.com/arth-shukla/mshab
공식 코드ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks
https://github.com/arthurxlw/cytonRL
공식 코드CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++
https://github.com/artifitialleap-mbzuai/rl-p2p-price-prediction
공식 코드Energy Pricing in P2P Energy Systems Using Reinforcement Learning
https://github.com/aryan-garg/pecnet-pedestrian-trajectory-prediction
공식 코드G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System
https://github.com/asappresearch/dialogue-offline-rl
공식 코드On the Effectiveness of Offline RL for Dialogue Response Generation
https://github.com/ashishp166/Noisy-Zero-Shot-Coordination
공식 코드https://github.com/ashusao/pcrl
공식 코드Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks
https://github.com/asikist/nnc
공식 코드Neural Ordinary Differential Equation Control of Dynamics on Graphs
https://github.com/aslanides/aixijs
공식 코드Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
https://github.com/asneha213/spiking-agent-RL
공식 코드https://github.com/asonabend/ESRL
공식 코드Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
https://github.com/asperti/microracer
공식 코드MicroRacer: a didactic environment for Deep Reinforcement Learning
https://github.com/assafbk/mocha_code
공식 코드https://github.com/astooke/rlpyt
공식 코드rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
https://github.com/astral-group/data-efficient-llm-rl
공식 코드https://github.com/astrid-merckling/SRL4RL
공식 코드https://github.com/aswinpaul/iwai2021_aisc
공식 코드https://github.com/atakandag/multi-objective-sim2real
공식 코드Monolithic vs. hybrid controller for multi-objective Sim-to-Real learning
https://github.com/atavakol/action-branching-agents
공식 코드Action Branching Architectures for Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.