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/changchencc/plandq
공식 코드PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer
https://github.com/changmin-yu/ccwm_code
공식 코드Learning State Representations via Retracing in Reinforcement Learning
https://github.com/clic-lab/drif
공식 코드Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction
https://github.com/cloud0723/offline-mlirl
공식 코드https://github.com/combra-lab/pop-spiking-deep-rl
공식 코드Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
https://github.com/conglu1997/v-d4rl
공식 코드Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
https://github.com/contactrika/bulb
공식 코드Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
https://github.com/core-robotics-lab/icct
공식 코드Learning Interpretable, High-Performing Policies for Autonomous Driving
https://github.com/cosynus-lix/STAR
공식 코드Reconciling Spatial and Temporal Abstractions for Goal Representation
https://github.com/cx441000319/drc
공식 코드The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards
https://github.com/danielpalen/value_expansion
공식 코드Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
https://github.com/danijar/dreamer
공식 코드https://github.com/dee0512/Temporally-Layered-Architecture
공식 코드Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures
https://github.com/deepmind/dm_control
공식 코드https://github.com/deepmind/spriteworld
공식 코드https://github.com/denisyarats/drq
공식 코드Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
https://github.com/denisyarats/proto
공식 코드https://github.com/desikrengarajan/fedora
공식 코드https://github.com/dhruvms/DenseTrafficEval
공식 코드Driving in Dense Traffic with Model-Free Reinforcement Learning
https://github.com/dhruvms/HighwayTraffic
공식 코드Driving in Dense Traffic with Model-Free Reinforcement Learning
https://github.com/dig-beihang/ami
공식 코드Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
https://github.com/dkangin/baselines
공식 코드On-Policy Trust Region Policy Optimisation with Replay Buffers
https://github.com/dlpbc/nm-metarl
공식 코드https://github.com/dmksjfl/DARC
공식 코드Efficient Continuous Control with Double Actors and Regularized Critics
https://github.com/dmksjfl/GD3
공식 코드https://github.com/dyne-submission/dynamics-aware-embeddings
공식 코드https://github.com/ed2-paper/ed2
공식 코드Continuous Control With Ensemble Deep Deterministic Policy Gradients
https://github.com/ed2-source-code/ed2
공식 코드ED2: Environment Dynamics Decomposition World Models for Continuous Control
https://github.com/electronicarts/character-motion-vaes
공식 코드https://github.com/elementai/avenue
공식 코드https://github.com/eleurent/highway-env
공식 코드Understanding and Mitigating the Limitations of Prioritized Experience Replay
https://github.com/ermongroup/MetaIRL
공식 코드Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
https://github.com/existentialrobotics/rl_active_multi_target_tracking
공식 코드Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories
https://github.com/fabiopardo/tonic
공식 코드Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking
https://github.com/facebookresearch/WhereDidMyOptimumGo
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.