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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

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/patrik-ha/explainable-minichess

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Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts

https://github.com/paulo-al-castro/mt5se

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mt5se: An Open Source Framework for Building Autonomous Trading Robots

https://github.com/paulorocosta/learning-2opt-drl

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Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning

https://github.com/pbecker93/coral

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Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL

https://github.com/pbecker93/vrkn

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On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

https://github.com/pcheng2/tsrl

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Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

https://github.com/pearl-utexas/sope

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SOPE: Spectrum of Off-Policy Estimators

https://github.com/peggy4444/soccer_rl

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Towards optimized actions in critical situations of soccer games with deep reinforcement learning

https://github.com/peidehuang/gradient

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Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

https://github.com/pemami4911/deep-rl

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Deep Reinforcement Learning with Feedback-based Exploration

https://github.com/pengyang7881187/fedrl

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Federated Reinforcement Learning with Environment Heterogeneity

https://github.com/pengyanghua/DL2

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DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters

https://github.com/peteanderson80/Matterport3DSimulator

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Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

https://github.com/petergriffinjin/search-r1

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Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

https://github.com/petrosgk/AudioRL

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A Deep Reinforcement Learning Approach to Audio-Based Navigation in a Multi-Speaker Environment

https://github.com/petrosgk/sed_rl_post_processing

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Improving Post-Processing of Audio Event Detectors Using Reinforcement Learning

https://github.com/peweetheman/reinforcement_learning_in_two_player_simultaneous_action_games

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Reinforcement Learning In Two Player Zero Sum Simultaneous Action Games

https://github.com/peymantehrani/fdrl-pc-dyspan

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Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks

https://github.com/pfnet-research/piekd

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Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning

https://github.com/phate09/safedrl

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Verified Probabilistic Policies for Deep Reinforcement Learning

https://github.com/philippaltmann/crop

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CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing

https://github.com/philippaltmann/qcd

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Challenges for Reinforcement Learning in Quantum Circuit Design

https://github.com/pi-star-lab/jirl

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A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret

https://github.com/pierthodo/temporal_regularization

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Temporal Regularization in Markov Decision Process

https://github.com/pikaju/muzero-g

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Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

https://github.com/pipixiaqishi1/tatu

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Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning

https://github.com/pittcps/rl-parking

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Energy-Efficient Parking Analytics System using Deep Reinforcement Learning

https://github.com/piyush-j/bertrlfuzzer

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BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer

https://github.com/pkiourti/rl_backdoor

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TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents

https://github.com/pku-ai-edge/corro

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Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning

https://github.com/pku-ai-edge/roadnetsz

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Learning to Share in Multi-Agent Reinforcement Learning

https://github.com/pku-alignment/omnisafe

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Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation

https://github.com/pku-alignment/safedreamer

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SafeDreamer: Safe Reinforcement Learning with World Models

https://github.com/pku-marl/dexteroushands

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Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

https://github.com/pku-marl/multi-agent-transformer

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Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

https://github.com/pku-rl/adarefiner

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AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback

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