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Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bo-pang/olsbpi
공식 코드Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems
https://github.com/boettiger-lab/approx-model-or-approx-soln
공식 코드Pretty darn good control: when are approximate solutions better than approximate models
https://github.com/boettiger-lab/rl-intro
공식 코드https://github.com/borealisai/sasrec-ccql
공식 코드Robust Reinforcement Learning Objectives for Sequential Recommender Systems
https://github.com/borgr/DORA
공식 코드DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
https://github.com/boschresearch/DD_OPG
공식 코드https://github.com/boschresearch/MetaBO
공식 코드Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
https://github.com/boschresearch/dist-mbrl
공식 코드https://github.com/boschresearch/trust-region-layers
공식 코드Differentiable Trust Region Layers for Deep Reinforcement Learning
https://github.com/boschresearch/ube-mbrl
공식 코드https://github.com/bossdm/LifelongRL
공식 코드https://github.com/bowenliu16/rl_graph_generation
공식 코드Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
https://github.com/braemt/attentive-multi-task-deep-reinforcement-learning
공식 코드https://github.com/brain-research/batch-ppo
공식 코드TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow
https://github.com/brain-research/guided-evolutionary-strategies
공식 코드Guided evolutionary strategies: Augmenting random search with surrogate gradients
https://github.com/bramgrooten/automatic-noise-filtering
공식 코드Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
https://github.com/bramgrooten/deeprl-for-hanabi
공식 코드Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi
https://github.com/bramgrooten/mask-distractions
공식 코드MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning
https://github.com/brendenpetersen/deep-symbolic-regression
공식 코드https://github.com/brenting/RL-negotiation
공식 코드Towards General Negotiation Strategies with End-to-End Reinforcement Learning
https://github.com/brett-daley/dqn-lambda
공식 코드https://github.com/brett-daley/fast-dqn
공식 코드https://github.com/brett-daley/trajectory-aware-etraces
공식 코드Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
https://github.com/brett-daley/virtual-replay-cache
공식 코드https://github.com/brucegeli/top_erl_iclr25
공식 코드TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning
https://github.com/bruceyg/dpo-mbr
공식 코드Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding
https://github.com/bsen/performative-rl-gradually-shifting-envs
공식 코드Performative Reinforcement Learning in Gradually Shifting Environments
https://github.com/bstadie/third_person_im
공식 코드https://github.com/buaawgj/lope
공식 코드Preference-Guided Reinforcement Learning for Efficient Exploration
https://github.com/buaawgj/tace
공식 코드Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
https://github.com/buds-lab/recommender-sys-for-buildings-textmining-review
공식 코드https://github.com/buptlxh/conformer-rlpatching
공식 코드https://github.com/burcukoglu/p4o-predictiveprocessingppo
공식 코드Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
https://github.com/butterfliesss/EmpRL
공식 코드Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation
https://github.com/bytarnish/agile
공식 코드AGILE: A Novel Reinforcement Learning Framework of LLM Agents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.