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/ml-jku/helm
공식 코드History Compression via Language Models in Reinforcement Learning
https://github.com/ml-jku/l2m
공식 코드https://github.com/ml-jku/lram
공식 코드A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
https://github.com/ml-jku/reactive-exploration
공식 코드Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
https://github.com/ml-jku/rudder
공식 코드https://github.com/ml-research/expil
공식 코드EXPIL: Explanatory Predicate Invention for Learning in Games
https://github.com/ml-research/rational_activations
공식 코드Adaptive Rational Activations to Boost Deep Reinforcement Learning
https://github.com/ml-research/rational_rl
공식 코드Adaptive Rational Activations to Boost Deep Reinforcement Learning
https://github.com/ml-research/rational_sl
공식 코드Adaptive Rational Activations to Boost Deep Reinforcement Learning
https://github.com/mld3/counterfactualannot-semiope
공식 코드Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
https://github.com/mld3/offlinerl_factoredactions
공식 코드Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
https://github.com/mlech26l/bayesian_nn_safety
공식 코드https://github.com/mlech26l/neuronal_circuit_policies
공식 코드https://github.com/mlii/mfrl
공식 코드https://github.com/mlpc-ucsd/xtra
공식 코드On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
https://github.com/mlpeschl/moral_rl
공식 코드MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning
https://github.com/mlsm-at-dtu/floods_transport_rl
공식 코드https://github.com/mm-thinking/metis-rise
공식 코드Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model Learning
https://github.com/mm-vl/ulm-r1
공식 코드Co-Reinforcement Learning for Unified Multimodal Understanding and Generation
https://github.com/mmilk1231/MirrorDescentSearch
공식 코드https://github.com/mmorris44/expressive-gdns
공식 코드Universally Expressive Communication in Multi-Agent Reinforcement Learning
https://github.com/mnoukhov/elastic-reset
공식 코드https://github.com/modalminds/mm-eureka
공식 코드MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning
https://github.com/modanesh/anomalous_rl_envs
공식 코드Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results
https://github.com/modanesh/recurrent_implicit_quantile_networks
공식 코드Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results
https://github.com/modestyachts/ARS
공식 코드Simple random search provides a competitive approach to reinforcement learning
https://github.com/mohammadrezanakhaei/relce
공식 코드Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
https://github.com/mohmdelsayed/streaming-drl
공식 코드https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics
공식 코드https://github.com/montrealrobotics/iv_rl
공식 코드Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
https://github.com/moonshotai/kimi-vl
공식 코드https://github.com/moporgic/tdl2048
공식 코드https://github.com/moreanp/osc
공식 코드Policy Constraint by Only Support Constraint for Offline Reinforcement Learning
https://github.com/morgancthomas/molscore
공식 코드ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
https://github.com/morning9393/etpo
공식 코드Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.