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/amazon-research/replay-based-recurrent-rl
공식 코드Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
https://github.com/amazon-science/replay-based-recurrent-rl
공식 코드Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
https://github.com/amir-karimi96/continuous-time-continuous-option-policy-gradient
공식 코드https://github.com/anandkamat05/TDEOC
공식 코드https://github.com/anndvision/vsop
공식 코드ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
https://github.com/anonicml2019/icml_2019_state_abstraction
공식 코드Learning State Abstractions for Transfer in Continuous Control
https://github.com/apexrl/coil
공식 코드https://github.com/architsharma97/medal
공식 코드A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
https://github.com/arpit15/skill-based-exploration-drl
공식 코드Model Learning for Look-ahead Exploration in Continuous Control
https://github.com/asperti/microracer
공식 코드MicroRacer: a didactic environment for Deep Reinforcement Learning
https://github.com/atavakol/action-branching-agents
공식 코드Action Branching Architectures for Deep Reinforcement Learning
https://github.com/atavakol/action-hypergraph-networks
공식 코드Learning to Represent Action Values as a Hypergraph on the Action Vertices
https://github.com/automl/TempoRL
공식 코드https://github.com/aviralkumar2907/CQL
공식 코드https://github.com/awskhalil/oscar
공식 코드https://github.com/baimingc/dambrl
공식 코드Delay-Aware Model-Based Reinforcement Learning for Continuous Control
https://github.com/baitingluo/l-map
공식 코드Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
https://github.com/batra-mlp-lab/va_mbrl
공식 코드https://github.com/baturaysaglam/ac-off-poc
공식 코드https://github.com/baturaysaglam/dase
공식 코드Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms
https://github.com/baturaysaglam/discover
공식 코드Deep Intrinsically Motivated Exploration in Continuous Control
https://github.com/baturaysaglam/la3p
공식 코드https://github.com/baturaysaglam/swtd3
공식 코드https://github.com/bellmantimehut/dipo
공식 코드Policy Representation via Diffusion Probability Model for Reinforcement Learning
https://github.com/bluecontra/AAAI2021-VDFP
공식 코드Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
https://github.com/bmazoure/sparseMuJoCo
공식 코드Leveraging exploration in off-policy algorithms via normalizing flows
https://github.com/boschresearch/DD_OPG
공식 코드https://github.com/boschresearch/dist-mbrl
공식 코드https://github.com/boschresearch/ube-mbrl
공식 코드https://github.com/caelan/SS-Replan
공식 코드Online Replanning in Belief Space for Partially Observable Task and Motion Problems
https://github.com/calvincbzhang/orso
공식 코드ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization
https://github.com/camall3n/markov-state-abstractions
공식 코드Learning Markov State Abstractions for Deep Reinforcement Learning
https://github.com/causalml/bcrl
공식 코드Learning Bellman Complete Representations for Offline Policy Evaluation
https://github.com/cccedric/cpql
공식 코드https://github.com/ceisenach/MPG
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.