Continuous Control
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 73
- 보존된 평가 기록
- 116
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/google/brain-tokyo-workshop/tree/master/WANNRelease
https://github.com/google/evojax
Simple random search provides a competitive approach to reinforcement learning
https://github.com/google/trax
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
https://github.com/gowriaddepalli/ML_DL_Research_collab_base
Deep Reinforcement Learning framework for Autonomous Driving
https://github.com/gstoica27/cpg_ppo
https://github.com/gtrll/dartenv
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/guillaumeboniface/reacher
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/gunjan99/Review-of-World-Models-using-Latex
https://github.com/guofeng201507/ISA-IPA-2020-05-05-IS1PT-GRP-High5-StockTrading
https://github.com/gwbcho/dpo-replication
Distributional Policy Optimization: An Alternative Approach for Continuous Control
https://github.com/gwthomas/gtml
https://github.com/h-aboutalebi/SparceReward
https://github.com/halajadallah/RL-Quadcopter_project
https://github.com/hamidthri/navbot_ppo
https://github.com/hamishs/JAX-RL
https://github.com/haosulab/ManiSkill-Learn
https://github.com/hardmaru/WorldModelsExperiments
https://github.com/hari-sikchi/loop
https://github.com/harruff/Senior_Project_Repository
https://github.com/hdilab/world_model_experiments
https://github.com/hdparks/AsteroidsDeepReinforcement
https://github.com/hebowei2000/deep-reinforcement-learning
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
https://github.com/hemilpanchiwala/Hindsight-Experience-Replay
https://github.com/hill-a/stable-baselines
https://github.com/hmhuy0/SIM-RL
https://github.com/hsgrandhi/AIProject
https://github.com/huanzhang12/sa_ppo
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
https://github.com/hybridrobotics/car-racing
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
https://github.com/hyunin-lee/ForecasterSAC
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient
https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement
https://github.com/iclavera/learning_to_adapt
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/ifestus/rl
https://github.com/ikostrikov/cql-results
https://github.com/ikostrikov/jax-rl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.