https://github.com/facebookresearch/habitat-lab
공식 코드Habitat 2.0: Training Home Assistants to Rearrange their Habitat
Papers with Code 아카이브
Reinforcement Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 162
- 보존된 평가 기록
- 1
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/icp-block-mdp
공식 코드https://github.com/facebookresearch/image-goal-nav-dataset
공식 코드Memory-Augmented Reinforcement Learning for Image-Goal Navigation
https://github.com/facebookresearch/impact-driven-exploration
공식 코드RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments
https://github.com/facebookresearch/level-replay
공식 코드https://github.com/facebookresearch/mbrl-lib
공식 코드MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
https://github.com/facebookresearch/measuring-emergent-comm
공식 코드https://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/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/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/rlcd
공식 코드RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment
https://github.com/facebookresearch/salina
공식 코드Learning a subspace of policies for online adaptation in Reinforcement Learning
https://github.com/facebookresearch/sound-spaces
공식 코드https://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/facebookresearch/transformer-sequential
공식 코드Addressing Some Limitations of Transformers with Feedback Memory
https://github.com/falcondai/loop-estimator
공식 코드Loop Estimator for Discounted Values in Markov Reward Processes
https://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/metaworld
공식 코드https://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
https://github.com/farquhar13/rl_transmission_control
공식 코드Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.