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/chandu-97/BayesByBackprop
https://github.com/chaneeh/SeqGAN_experiment
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/changpowei/MicroRTS
Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
https://github.com/chanlaw/assistive-bandits
https://github.com/chariff/GPro
https://github.com/charleshsc/HarmoDT
Q-value Regularized Transformer for Offline Reinforcement Learning
https://github.com/charleshsc/qt
Q-value Regularized Transformer for Offline Reinforcement Learning
https://github.com/chauncygu/safe-multi-agent-isaac-gym
https://github.com/chauncygu/safe-multi-agent-mujoco
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/chauncygu/safe-multi-agent-robosuite
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/chengyu2/vizdoom_rl_community_canberra
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
https://github.com/chenhongge/SA_DQN
Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger
https://github.com/chinganc/lightatac
Adversarially Trained Actor Critic for Offline Reinforcement Learning
https://github.com/christiancosgrove/cs767final
https://github.com/chung771026/Implement-seqGAN-with-Keras
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/cindycia/Atari-SAC-Discrete
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/cjlovering/Towards-Interpretable-Reinforcement-Learning-Using-Attention-Augmented-Agents-Replication
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
https://github.com/cjohnchen/changebale
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/cjohnchen/mirror
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/cjohnchen/sai
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/cjy1992/gym-carla
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
https://github.com/ckinateder/pytsetlinmachineparallel
https://github.com/ckjzsa/wwtp_opt_rl
https://github.com/clarky104/carla_aebs
Dueling Network Architectures for Deep Reinforcement Learning
https://github.com/claudeHifly/BipedalWalker-v3
https://github.com/clear-nus/MuMMI
https://github.com/clementbernardd/Count-Based-Exploration
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
https://github.com/clic-lab/ciff
Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents
https://github.com/clin366/MFN
Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning
https://github.com/clinplayer/3DModelingRL
https://github.com/clrrrr/promp_plus
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/clvrai/furniture
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
https://github.com/clvrai/skild
Demonstration-Guided Reinforcement Learning with Learned Skills
https://github.com/cmu-safari/hermes
Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning
https://github.com/coco66/ADFQ
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.