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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/ai4ai-lab/factored-action-spaces-for-ope

공식 코드

Leveraging Factored Action Spaces for Off-Policy Evaluation

https://github.com/ai4ce/SNAC

공식 코드

Simultaneous Navigation and Construction Benchmarking Environments

https://github.com/ai4co/rl4co

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RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

https://github.com/ai4finance-foundation/elegantrl

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ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning

https://github.com/ai4finance-foundation/finrl-meta

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FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance

https://github.com/ai4pdlab/dpo_plm

공식 코드

Guiding Generative Protein Language Models with Reinforcement Learning

https://github.com/ai4society/infospread-neurips-24

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Towards Effective Planning Strategies for Dynamic Opinion Networks

https://github.com/ai9stars/autotriton

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AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs

https://github.com/aicenter/openspiel_reproductions

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Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report

https://github.com/aidaafshar/delf

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DeLF: Designing Learning Environments with Foundation Models

https://github.com/aidaafshar/learning-rate-free-reinforcement-learning

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Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives

https://github.com/aidanscannell/dcmpc

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Discrete Codebook World Models for Continuous Control

https://github.com/aidc-ai/marco-o1

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Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions

https://github.com/aidefender/remern-remert

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Regret Minimization Experience Replay in Off-Policy Reinforcement Learning

https://github.com/aidefender/tscac

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Two-Stage Constrained Actor-Critic for Short Video Recommendation

https://github.com/aielawady/relic

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ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI

https://github.com/aiframeresearch/spo

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Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models

https://github.com/aikkala/O2MConverter

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Converting Biomechanical Models from OpenSim to MuJoCo

https://github.com/aim-uofa/omni-r1

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Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration

https://github.com/ainfosec/codex

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CODEX: A Cluster-Based Method for Explainable Reinforcement Learning

https://github.com/airi-institute/ffreed

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FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction

https://github.com/aiueola/kdd2023-aips

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Off-Policy Evaluation of Ranking Policies under Diverse User Behavior

https://github.com/aiueola/neurips2023-future-dependent-ope

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Future-Dependent Value-Based Off-Policy Evaluation in POMDPs

https://github.com/aiueola/webconf2024-slate-ope-via-abstraction

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Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction

https://github.com/aiueola/wsdm2022-cascade-dr

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Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model

https://github.com/aizazsharif/benchmarking-qrs-2022

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Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment

https://github.com/akhileshraj91/generalized_rl_anl

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A Reinforcement Learning Approach for Performance-aware Reduction in Power Consumption of Data Center Compute Nodes

https://github.com/akifumi-wachi-4/safe_near_optimal_mdp

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Safe Reinforcement Learning in Constrained Markov Decision Processes

https://github.com/akifumi-wachi-4/spolf

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Safe Policy Optimization with Local Generalized Linear Function Approximations

https://github.com/akjayant/mbppol

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Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm

https://github.com/aklein1995/intrinsic_motivation_techniques_study

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An Evaluation Study of Intrinsic Motivation Techniques applied to Reinforcement Learning over Hard Exploration Environments

https://github.com/akomandu/L1SVMIRL

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On the Correctness and Sample Complexity of Inverse Reinforcement Learning

https://github.com/akrouriad/tpami_metricrl

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Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts

https://github.com/akuxcw/GridMask

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GridMask Data Augmentation

https://github.com/aladoro/stabilizing-off-policy-rl

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Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

https://github.com/albertcity/opa

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Online Prototype Alignment for Few-shot Policy Transfer

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