https://github.com/facebookresearch/gala
공식 코드Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
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/facebookresearch/galactic
공식 코드Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second
https://github.com/facebookresearch/gen_dgrl
공식 코드https://github.com/facebookresearch/habitat-lab
공식 코드Habitat 2.0: Training Home Assistants to Rearrange their Habitat
https://github.com/facebookresearch/higher
공식 코드https://github.com/facebookresearch/how-to-autorl
공식 코드Hyperparameters in Reinforcement Learning and How To Tune Them
https://github.com/facebookresearch/hsd3
공식 코드https://github.com/facebookresearch/humenv
공식 코드Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
https://github.com/facebookresearch/icp-block-mdp
공식 코드Learning Domain Invariant Representations in Goal-conditioned Block MDPs
https://github.com/facebookresearch/image-goal-nav-dataset
공식 코드Memory-Augmented Reinforcement Learning for Image-Goal Navigation
https://github.com/facebookresearch/mbrl-lib
공식 코드MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
https://github.com/facebookresearch/metamotivo
공식 코드Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
https://github.com/facebookresearch/minihack
공식 코드MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
https://github.com/facebookresearch/modem
공식 코드MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
https://github.com/facebookresearch/motif
공식 코드Motif: Intrinsic Motivation from Artificial Intelligence Feedback
https://github.com/facebookresearch/mtenv
공식 코드Multi-Task Reinforcement Learning with Context-based Representations
https://github.com/facebookresearch/mtrl
공식 코드Multi-Task Reinforcement Learning with Context-based Representations
https://github.com/facebookresearch/neural-rewriter
공식 코드Learning to Perform Local Rewriting for Combinatorial Optimization
https://github.com/facebookresearch/nle
공식 코드https://github.com/facebookresearch/oni
공식 코드Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
https://github.com/facebookresearch/online-dt
공식 코드https://github.com/facebookresearch/pearl
공식 코드https://github.com/facebookresearch/ppo-dice
공식 코드Stable Policy Optimization via Off-Policy Divergence Regularization
https://github.com/facebookresearch/rebel
공식 코드Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
https://github.com/facebookresearch/reward-estimator-corl
공식 코드Reward Estimation for Variance Reduction in Deep Reinforcement Learning
https://github.com/facebookresearch/salina
공식 코드Learning a subspace of policies for online adaptation in Reinforcement Learning
https://github.com/facebookresearch/ssorl
공식 코드Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
https://github.com/facebookresearch/svg
공식 코드On the model-based stochastic value gradient for continuous reinforcement learning
https://github.com/facebookresearch/td-delta
공식 코드https://github.com/falsr/FALSR
공식 코드Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
https://github.com/familyld/insightfair
공식 코드What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
https://github.com/fangchenliu/maskdp_public
공식 코드Masked Autoencoding for Scalable and Generalizable Decision Making
https://github.com/farama-foundation/gymnasium
공식 코드Gymnasium: A Standard Interface for Reinforcement Learning Environments
https://github.com/farama-foundation/momaland
공식 코드MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
https://github.com/farismismar/Deep-Q-Learning-SON-Perf-Improvement
공식 코드A Framework for Automated Cellular Network Tuning with Reinforcement Learning
https://github.com/farismismar/Q-Learning-Power-Control
공식 코드A Framework for Automated Cellular Network Tuning with Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.