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/coldsummerday/SD-SAC
공식 코드https://github.com/combra-lab/pop-spiking-deep-rl
공식 코드Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
https://github.com/compdyn/rl_grid_coarsen
공식 코드Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
https://github.com/complementizer/rl-sentence-compression
공식 코드Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
https://github.com/compsoftmatterbiophysics-cityu-hk/applying-drl-to-hp-model-for-protein-structure-prediction
공식 코드Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
https://github.com/conglu1997/SynthER
공식 코드https://github.com/conglu1997/nppac
공식 코드On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
https://github.com/conglu1997/v-d4rl
공식 코드Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
https://github.com/connor-vr/Interpretable-Sub-Goal-RL
공식 코드A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goals
https://github.com/contactrika/bulb
공식 코드Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
https://github.com/continualai/avalanche-rl
공식 코드https://github.com/convlab/convlab-3
공식 코드ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
https://github.com/coodest/highway-rl
공식 코드https://github.com/coodest/highwayRL
공식 코드https://github.com/cool-rr/chicken-coop
공식 코드Emergent Dominance Hierarchies in Reinforcement Learning Agents
https://github.com/cooper-org/cooper
공식 코드https://github.com/corail-research/SeaPearl.jl
공식 코드SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning
https://github.com/core-robotics-lab/icct
공식 코드Learning Interpretable, High-Performing Policies for Autonomous Driving
https://github.com/core-robotics-lab/opponent-modeling
공식 코드https://github.com/corentinlger/er-mrl
공식 코드https://github.com/cornell-rl/drpo
공식 코드https://github.com/cornell-rl/tril
공식 코드https://github.com/cps11/darl
공식 코드Reinforcement Learning for Few-Shot Text Generation Adaptation
https://github.com/cr-gjx/ria
공식 코드https://github.com/cranial-xix/marl-copa
공식 코드Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition
https://github.com/cranial-xix/metric-residual-network
공식 코드Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning
https://github.com/crgagne/twosteps_neurips2021
공식 코드https://github.com/crisbodnar/pderl
공식 코드https://github.com/cristianocapone/biodreaming
공식 코드Towards biologically plausible Dreaming and Planning in recurrent spiking networks
https://github.com/criteo-research/reco-gym
공식 코드https://github.com/cselab/smarties
공식 코드https://github.com/csgwon/AWS-RL-Env
공식 코드Automated Cloud Provisioning on AWS using Deep Reinforcement Learning
https://github.com/csiro-mlai/RL-BOED
공식 코드Optimizing Sequential Experimental Design with Deep Reinforcement Learning
https://github.com/csmile-1006/preferencetransformer
공식 코드Preference Transformer: Modeling Human Preferences using Transformers for RL
https://github.com/csml-iit-ucl/powr
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.