https://github.com/dimits-ts/synthetic_moderation_experiments
공식 코드Scalable Evaluation of Online Facilitation Strategies via Synthetic Simulation of Discussions
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/dimplekochar/LEDRO
공식 코드LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits
https://github.com/dingyiming0427/goalgail
공식 코드https://github.com/diplomacy/research
공식 코드https://github.com/distributionnetworkstudelft/ev2gym
공식 코드EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking
https://github.com/divnori/protac-design
공식 코드De novo PROTAC design using graph-based deep generative models
https://github.com/djole/IR2L
공식 코드Safer Reinforcement Learning through Transferable Instinct Networks
https://github.com/djstrouse/InfoMARL
공식 코드Learning to Share and Hide Intentions using Information Regularization
https://github.com/dkangin/baselines
공식 코드On-Policy Trust Region Policy Optimisation with Replay Buffers
https://github.com/dkkim93/further
공식 코드Influencing Long-Term Behavior in Multiagent Reinforcement Learning
https://github.com/dkkim93/meta-mapg
공식 코드A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
https://github.com/dlpbc/mask-lrl
공식 코드https://github.com/dlpbc/nm-metarl
공식 코드https://github.com/dlpbc/penn-a
공식 코드Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
https://github.com/dmhyun/msrp
공식 코드https://github.com/dmitrySorokin/interferobotProject
공식 코드Interferobot: aligning an optical interferometer by a reinforcement learning agent
https://github.com/dmitrykazhdan/MARLeME
공식 코드MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
https://github.com/dmksjfl/CABI
공식 코드https://github.com/dmksjfl/DARC
공식 코드Efficient Continuous Control with Double Actors and Regularized Critics
https://github.com/dmksjfl/mcq
공식 코드Mildly Conservative Q-Learning for Offline Reinforcement Learning
https://github.com/dmksjfl/par
공식 코드Cross-Domain Policy Adaptation by Capturing Representation Mismatch
https://github.com/dmksjfl/seabo
공식 코드SEABO: A Simple Search-Based Method for Offline Imitation Learning
https://github.com/dmollaaliod/alta2017-rl
공식 코드https://github.com/dmollaaliod/bioasq8b-public
공식 코드Query Focused Multi-document Summarisation of Biomedical Texts
https://github.com/dnandha/mopac
공식 코드Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
https://github.com/dniku/free-lunch-saliency
공식 코드https://github.com/dnishio/DSAC
공식 코드https://github.com/dnishio/NEC-RP
공식 코드https://github.com/docallaghan/tunable-agents
공식 코드Exploring the Impact of Tunable Agents in Sequential Social Dilemmas
https://github.com/doerlbh/UnsupervisedAttentionMechanism
공식 코드Unsupervised Attention Mechanism across Neural Network Layers
https://github.com/doerlbh/dilemmaRL
공식 코드Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior
https://github.com/doerlbh/mentalRL
공식 코드A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry
https://github.com/dojeon-ai/atari-pb
공식 코드Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
https://github.com/dojeon-ai/plastic
공식 코드PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
https://github.com/dojeon-ai/simtpr
공식 코드https://github.com/dongchen06/marl_cavs
공식 코드Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.