https://github.com/tombewley/TripleTree
공식 코드TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
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/tomdbar/eco-dqn
공식 코드Exploratory Combinatorial Optimization with Reinforcement Learning
https://github.com/tomdbar/ecord
공식 코드Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration
https://github.com/tomjur/ModelBasedDDPG
공식 코드Harnessing Reinforcement Learning for Neural Motion Planning
https://github.com/tomkingsforduoa/social-reward-function
공식 코드A General, Evolution-Inspired Reward Function for Social Robotics
https://github.com/tommasomarzi/fgrl
공식 코드https://github.com/tomsilver/pddlgym
공식 코드https://github.com/tongyi-zhiwen/qwenlong-l1
공식 코드QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
https://github.com/toni-sm/skrl
공식 코드skrl: Modular and Flexible Library for Reinforcement Learning
https://github.com/tonyzhaozh/meld
공식 코드MELD: Meta-Reinforcement Learning from Images via Latent State Models
https://github.com/traffic-alpha/illm-tsc
공식 코드https://github.com/traffic-alpha/llm-assisted-light
공식 코드https://github.com/trality/fire
공식 코드https://github.com/translearn/safeMotions
공식 코드Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors
https://github.com/trevormcinroe/pixelbrax
공식 코드PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU
https://github.com/trustai/certifycmarl
공식 코드Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning
https://github.com/trustai/receps
공식 코드Reward Certification for Policy Smoothed Reinforcement Learning
https://github.com/trustairlab/safervlm
공식 코드Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities
https://github.com/truthless11/GDPL
공식 코드Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
https://github.com/truthless11/HRL-RE
공식 코드A Hierarchical Framework for Relation Extraction with Reinforcement Learning
https://github.com/truthless11/MADPL
공식 코드Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition
https://github.com/trzhang0116/HRAC
공식 코드Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
https://github.com/tsinghuac3i/awesome-rl-reasoning-recipes
공식 코드https://github.com/tspooner/rl_markets
공식 코드https://github.com/tspooner/rmm.arl
공식 코드https://github.com/tsume82/Pavlov
공식 코드https://github.com/tsumers/rewards
공식 코드https://github.com/tsutikgiau/valuememorygraph
공식 코드Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning
https://github.com/ttotmoon/paco-mtrl
공식 코드PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
https://github.com/tttor/nbwpg
공식 코드https://github.com/tu-delft-ai-energy-lab/topgrid_morl
공식 코드Multi-Objective Reinforcement Learning for Power Grid Topology Control
https://github.com/tu2021/autothink
공식 코드Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL
https://github.com/tud-amr/parl
공식 코드Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
https://github.com/tudelft/risk-sensitive-rl
공식 코드https://github.com/tue-mps/damiirl
공식 코드Deep Adaptive Multi-Intention Inverse Reinforcement Learning
https://github.com/tulerfeng/video-r1
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.