https://github.com/NYCU-RL-Bandits-Lab/Q-Pensieve
공식 코드Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
Papers with Code 아카이브
Continuous Control
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Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Octavio-Pappalardo/Meta-learning-rewards
공식 코드Black box meta-learning intrinsic rewards for sparse-reward environments
https://github.com/Owen-Oertell/rlcm
공식 코드REBEL: Reinforcement Learning via Regressing Relative Rewards
https://github.com/PaoloP84/EfficacyModernES
공식 코드Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
https://github.com/QData/dmc_remastered
공식 코드Measuring Visual Generalization in Continuous Control from Pixels
https://github.com/RizanSM/zero_shot_llms_in_HIL_RL
공식 코드Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping
https://github.com/RockySJ/ampo
공식 코드Model-based Policy Optimization with Unsupervised Model Adaptation
https://github.com/SaminYeasar/Off_Policy_Adversarial_Inverse_Reinforcement_Learning
공식 코드https://github.com/SaminYeasar/sparse_reg
공식 코드Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
https://github.com/ShangtongZhang/DeepRL
공식 코드ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
https://github.com/ShawK91/erl_paper_nips18
공식 코드https://github.com/SilviaSapora/evil
공식 코드EvIL: Evolution Strategies for Generalisable Imitation Learning
https://github.com/SingerGao/ECAC
공식 코드https://github.com/StanfordASL/BaRC
공식 코드BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning
https://github.com/StanfordASL/SNDL
공식 코드Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
https://github.com/T3p/pois
공식 코드https://github.com/TakaOsa/adInfoHRL
공식 코드Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
https://github.com/TianhongDai/esil-hindsight
공식 코드https://github.com/UCLA-StarAI/Analogous-Disentangled-Actor-Critic
공식 코드Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration
https://github.com/Unity-Technologies/marathon-envs
공식 코드Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine
https://github.com/UoA-CARES/cares_reinforcement_learning/blob/main/cares_reinforcement_learning/algorithm/policy/CTD4.py
공식 코드CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics
https://github.com/WendyShang/dqn_zoo
공식 코드https://github.com/WendyShang/flare
공식 코드https://github.com/XuGW-Kevin/DrM
공식 코드DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
https://github.com/YaoYao1995/MEEE
공식 코드Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
https://github.com/Yusufma03/CVRL
공식 코드Contrastive Variational Reinforcement Learning for Complex Observations
https://github.com/ZhengyaoJiang/latentplan
공식 코드https://github.com/ZikangXiong/rl-detect-and-denoise-defense
공식 코드Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
https://github.com/adaptive-intelligent-robotics/DCRL-MAP-Elites
공식 코드Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
https://github.com/adaptive-intelligent-robotics/qdac
공식 코드https://github.com/adaptive-intelligent-robotics/qdax
공식 코드QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
https://github.com/adipandas/gym_multirotor
공식 코드https://github.com/adityab/crossq
공식 코드https://github.com/aidanscannell/dcmpc
공식 코드https://github.com/ait-lab/ggtde
공식 코드Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
https://github.com/amathislab/dmap
공식 코드DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.