https://github.com/andris955/diplomaterv
Actor-Mimic: Deep Multitask and Transfer 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/andyljones/cule
Accelerating Reinforcement Learning through GPU Atari Emulation
https://github.com/angelmtenor/RL-ROBOT
Towards a Common Implementation of Reinforcement Learning for Multiple Robotic Tasks
https://github.com/angelolovatto/raylab
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/anh-nn01/Lunar-Lander-Double-Deep-Q-Networks
https://github.com/anhtu293/NeurIPS-2019-Challenge
https://github.com/anita-hu/TF2-RL
https://github.com/ankur-rc/autodrive_ddpg
https://github.com/ankurhanda/rand_conv
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
https://github.com/anmold-07/physics-guided-blackout-mitigation
https://github.com/anonNo2/MulTeacher-KD
https://github.com/anonymous1234517/code
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/anoxia-1/Fault-Tolerant-Federated-Reinforcement-Learning-with-Theoretical-Guarantee-
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
https://github.com/antaradas94/MAML-waste-classification
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/anuragajay/d4rl
https://github.com/anyboby/ConstrainedMBPO
https://github.com/anyboby/mujoco_safety_gym
Safe Continuous Control with Constrained Model-Based Policy Optimization
https://github.com/apollopower/DOOM-AI
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
https://github.com/apoorvkhattar/RL-Project-2019
https://github.com/apourchot/ERL
https://github.com/apourchot/ERL-pytorch
https://github.com/apourchot/EvoLearn
https://github.com/apple/ml-reed
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
https://github.com/aqzheng/Adversarial-Learning-for-Neural-Dialogue-Generation
https://github.com/araffin/learning-to-drive-in-5-minutes
https://github.com/araffin/sbx
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/araffin/srl-zoo
Time-Contrastive Networks: Self-Supervised Learning from Video
https://github.com/aragakiyuiii/Gumbel-darts-master
https://github.com/aravic/generalizable-device-placement
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
https://github.com/aravind0706/neural-mpc
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/aravindsrinivas/curl_rainbow
https://github.com/aravindsrinivas/neural-mpc
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/arazd/ProgressivePrompts
https://github.com/archit120/Risk-Aware-Active-IRL
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
https://github.com/arcosin/Doric
https://github.com/argmax-ai/drone-hardware
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.