https://github.com/CausalML/IntrinsicallyEfficientStableOPE
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for 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/CausalML/MultipleLoggers
Optimal Off-Policy Evaluation from Multiple Logging Policies
https://github.com/Cejkis/I2A-Cejkis
Imagination-Augmented Agents for Deep Reinforcement Learning
https://github.com/Chan1998/MAAC
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/ChangyWen/wolpertinger_ddpg
https://github.com/CharlotteMorrison/Baxter-Research
https://github.com/CharlotteMorrison/Baxter-VREP
https://github.com/CharlotteMorrison/Baxter-VREP-Version-2
https://github.com/ChengshuLi/HRL4IN
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators
https://github.com/Chenkehan21/Adversarial-Attack-RL
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
https://github.com/Christopheraburns/openai-gym
https://github.com/ChristosKap/policy_consolidation
https://github.com/Clement-Hui/Q-Learning
https://github.com/Clyde21c/PEARL_bullet
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
https://github.com/CocoJam/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Codernauti/Exploration-of-DQN-in-CartPole-Environment
https://github.com/CoffeeddCat/Multiagent_chainMDP
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
https://github.com/ComarPers922/AI-based-3D-Game-Simulator
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/ContinualAI/avalanche
https://github.com/Cremiy/visdial-rl
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/Crevass/Hybrid-Agent
https://github.com/Curt-Park/rainbow-is-all-you-need
https://github.com/D-X-Y/AutoDL-Projects
https://github.com/D-X-Y/GDAS
https://github.com/D-X-Y/NAS-Projects
https://github.com/DHDev0/Muzero-unplugged
Online and Offline Reinforcement Learning by Planning with a Learned Model
https://github.com/DMIU-ShELL/MOSAIC
https://github.com/Daffan/ros_jackal
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
https://github.com/DalBigData/Vessel-RL-Environment
Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments
https://github.com/Dalkio/RL_rubiks
https://github.com/Dam930/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/Damcy/prioritized-experience-replay
https://github.com/DanielLSM/safe-rl-tutorial
https://github.com/DanielTakeshi/DCUR
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/DanielTakeshi/dqn
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
https://github.com/DartEnv/dart-env
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.