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/guiliang/cirl-benchmarks-public
공식 코드Benchmarking Constraint Inference in Inverse Reinforcement Learning
https://github.com/guiliang/icrl-benchmarks-public
공식 코드Benchmarking Constraint Inference in Inverse Reinforcement Learning
https://github.com/guillermoim/HRL_LMDP
공식 코드Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
https://github.com/gundoganalperen/DIRAL
공식 코드https://github.com/guolingbing/rlea
공식 코드https://github.com/guozheng-ma/da-in-visualrl
공식 코드A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
https://github.com/gustavokcouto/hgail
공식 코드https://github.com/guytenn/terminator
공식 코드https://github.com/gviejo/Gohal
공식 코드https://github.com/gwthomas/safe-mbpo
공식 코드https://github.com/gydpku/data_synthesis_rl
공식 코드https://github.com/haanvid/kmifqe
공식 코드Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
https://github.com/haanvid/kmis
공식 코드Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
https://github.com/haarnoja/sac
공식 코드Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/haarnoja/softqlearning
공식 코드https://github.com/hadijomaa/HypRL
공식 코드Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
https://github.com/hadizayer/autophoto
공식 코드AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning
https://github.com/hadrien-pouget/Ranking-Policy-Decisions
공식 코드https://github.com/hahahawu/long-to-short-via-model-merging
공식 코드Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
https://github.com/hahayonghuming/VDACs
공식 코드https://github.com/hai-h-nguyen/cosil-corl22
공식 코드Leveraging Fully Observable Policies for Learning under Partial Observability
https://github.com/haichao-zhang/pex
공식 코드Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
https://github.com/haidiazaman/rl-concept-learning-project
공식 코드Compositional Learning of Visually-Grounded Concepts Using Reinforcement
https://github.com/hakuhodo-technologies/scope-rl
공식 코드SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
https://github.com/han-cai/EAS
공식 코드https://github.com/han-cai/RL4AS_NetTrans
공식 코드https://github.com/han-cai/rlb-dp
공식 코드Real-Time Bidding by Reinforcement Learning in Display Advertising
https://github.com/hanbingyan/MRTR
공식 코드Can maker-taker fees prevent algorithmic cooperation in market making?
https://github.com/hanglai/autombpo
공식 코드On Effective Scheduling of Model-based Reinforcement Learning
https://github.com/hanglai/bmpo
공식 코드https://github.com/hanhananderson/pac-marl
공식 코드https://github.com/hanlinniu/turtlebot3_ddpg_collision_avoidance
공식 코드Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player
https://github.com/hanmochen/lux-open
공식 코드Emergent collective intelligence from massive-agent cooperation and competition
https://github.com/hanningzhang/er-prm
공식 코드https://github.com/hanshen95/penalized-bilevel-gradient-descent
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.