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/jingdic/rgmcomm
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/jingweiz/pytorch-dnc
https://github.com/jingwu6/handful-of-trials-in-pytorch
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/jingxixu/rl-vs-control
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?
https://github.com/jingyaogong/minimind
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
https://github.com/jingyi0000/R1-VL
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO
https://github.com/jj-zhu/resource_aware_control_rl
https://github.com/jjccero/pbrl/tree/master/pbrl/algorithms/ppg
https://github.com/jjccero/pbrl/tree/master/pbrl/algorithms/sac
https://github.com/jjenster/Test
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/jk96491/C-COMA
https://github.com/jk96491/SMAC
https://github.com/jkooy/NAS-UDA
https://github.com/jmribeiro/UGP
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning
https://github.com/jmvines20/ZhengyaoJiang-PGPortfolio
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
https://github.com/joanfmendo/Deep-Reinforcement-Learning-Algorithm-for-Dynamic-Pricing-of-Express-Lanes-with-Multiple-Access-Loca
https://github.com/joe-prog/https-github.com-facebookresearch-ParlAI
https://github.com/joeynmt/joeynmt
https://github.com/johannah/bootstrap_dqn
https://github.com/johannesharmse/multi_agent_RL
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/john-ai-lab/noisyrollout
NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation
https://github.com/johnmave126/rl-tracing
https://github.com/jojonki/hybrid-code-networks
https://github.com/jolibrain/wheatley
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
https://github.com/jollyraven100/Quant_algorithmic-trading_and_More
https://github.com/jollyraven100/Trade-Ideas-and-Research-Reference
https://github.com/jonah-chen/alphazero-guerzhoy
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/jonahs99/baby-zero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/jonasrothfuss/promp
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/jonathan-laurent/AlphaZero.jl
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/jonaths/dqn-grid
https://github.com/jonaths/tf-dqn
https://github.com/jondaa/CS236605FinalProject
Variance Networks: When Expectation Does Not Meet Your Expectations
https://github.com/joonleesky/train-procgen-pytorch
Leveraging Procedural Generation to Benchmark Reinforcement Learning
https://github.com/joshiatul/game_playing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.