https://github.com/atavakol/action-hypergraph-networks
공식 코드Learning to Represent Action Values as a Hypergraph on the Action Vertices
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/athar70/EDPCGRL
공식 코드https://github.com/athar70/edpcgrl4spider
공식 코드https://github.com/atishdixit16/ada_multigrid_ppo
공식 코드Robust optimal well control using an adaptive multi-grid reinforcement learning framework
https://github.com/atishdixit16/multilevel_ppo
공식 코드A Multilevel Reinforcement Learning Framework for PDE-based Control
https://github.com/atishdixit16/rl_robust_owc
공식 코드Stochastic optimal well control in subsurface reservoirs using reinforcement learning
https://github.com/atishdixit16/stable-baselines3
공식 코드A Multilevel Reinforcement Learning Framework for PDE-based Control
https://github.com/attilanagy234/enas-pytorch
공식 코드Improving the sample-efficiency of neural architecture search with reinforcement learning
https://github.com/autodeskailab/plotmap
공식 코드https://github.com/automl-private/crl_hpo
공식 코드https://github.com/automl/CARL
공식 코드CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
https://github.com/automl/HPO_for_RL
공식 코드On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
https://github.com/automl/SEARL
공식 코드https://github.com/automl/SPaCE
공식 코드Self-Paced Context Evaluation for Contextual Reinforcement Learning
https://github.com/automl/arlbench
공식 코드https://github.com/automl/autorl-landscape
공식 코드https://github.com/automl/instance-dac
공식 코드https://github.com/automl/learning_environments
공식 코드Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
https://github.com/automl/mdp-playground
공식 코드MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
https://github.com/autonomousvision/CaRL
공식 코드CaRL: Learning Scalable Planning Policies with Simple Rewards
https://github.com/avecplezir/realtime-agent
공식 코드https://github.com/aviralkumar2907/CQL
공식 코드https://github.com/avisingh599/reward-learning-rl
공식 코드End-to-End Robotic Reinforcement Learning without Reward Engineering
https://github.com/avivt/VIN
공식 코드https://github.com/awarelab/continual_world
공식 코드Continual World: A Robotic Benchmark For Continual Reinforcement Learning
https://github.com/awarelab/seed_rl
공식 코드Off-Policy Correction For Multi-Agent Reinforcement Learning
https://github.com/awesomelemon/pbt-nas
공식 코드https://github.com/awesomericky/multiple-gait-controller-for-quadruped-robot
공식 코드Learning multiple gaits of quadruped robot using hierarchical reinforcement learning
https://github.com/awjuliani/deep-rl-plasticity
공식 코드A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
https://github.com/awjuliani/neuro-nav
공식 코드Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
https://github.com/awslabs/or-rl-benchmarks
공식 코드ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
https://github.com/awslabs/sagemaker-battlesnake-ai
공식 코드Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop
https://github.com/axe76/QuadroPong
공식 코드An Efficient Application of Neuroevolution for Competitive Multiagent Learning
https://github.com/axelabels/DynMORL
공식 코드Dynamic Weights in Multi-Objective Deep Reinforcement Learning
https://github.com/axkoenig/grasp_refinement
공식 코드The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms
https://github.com/az79nefy/OrdinalRL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.