https://github.com/vwxyzjn/envpool-cleanrl
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
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/wangbx66/differentially-private-q-learning
Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces
https://github.com/wangclnlp/deepspeed-chat-extension
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
https://github.com/wangf622/GPLight
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN Prediction
https://github.com/wangml999/chess_zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/wangxiao5791509/Tracking-with-Deep-Reinforcement-Learning
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search
https://github.com/wangzizhao/Jointly-Learning-to-Construct-and-Control-Agents-using-Deep-Reinforcement-Learning
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
https://github.com/wassname/rl-portfolio-management
https://github.com/watakandai/hiro_pytorch
https://github.com/watchernyu/spinningup-drl-prototyping
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/waterhorse1/Pearl_relabel
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
https://github.com/wattlebirdaz/geql
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
https://github.com/wcarvalho/jaxneurorl
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/wenzhe-li/romi
Offline Reinforcement Learning with Reverse Model-based Imagination
https://github.com/wessle/multiagent
A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning
https://github.com/wgchang/PCGrad-pytorch-example
https://github.com/wgrathwohl/BackpropThroughTheVoidRL
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
https://github.com/wh1992v/R2RRMopionSolitaire
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
https://github.com/willspag/SeqGan
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/willwhitney/dynamics-aware-embeddings
https://github.com/willwhitney/pytorch_sac_lib
https://github.com/windstrip/DeepMind-StreetLearn
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
https://github.com/windstrip/PGPortfolio
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/wlzhang2020/reasonrag
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
https://github.com/wmol4/Pytorch_DDQN_Unity_Navigation
https://github.com/wongongv/mbpo_fork
https://github.com/wongongv/scnwithdqn
https://github.com/worldanon/learntopredict
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
https://github.com/wpiszlogin/driver_critic
https://github.com/wq13552463699/rrc_pos_ori
Solving the Real Robot Challenge using Deep Reinforcement Learning
https://github.com/wronnyhuang/SQLNet_inference
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/wsjeon/multiagent-particle-envs-maac
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/wsjeon/multiagent-particle-envs-v2
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/wtingda/DeepRLBreakout
https://github.com/wtliao/ImageTransformer
https://github.com/wuxx1016/Reinforcement-Learning-in-Finance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.