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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/yang1fan2/Dota2-Prediction

Real-time eSports Match Result Prediction

https://github.com/yaringal/HeteroscedasticDropoutUncertainty

Deep Exploration via Bootstrapped DQN

https://github.com/yaxinchen666/dce_pricingRL

Playing Atari with Deep Reinforcement Learning

https://github.com/yl3829/Q-map

Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

https://github.com/yoavalon/Quadcopter-env

Proximal Policy Optimization Algorithms

https://github.com/yoavalon/QuadcopterReinforcementLearning

Proximal Policy Optimization Algorithms

https://github.com/yogi2578/GameEngineLearning

Action-Conditional Video Prediction using Deep Networks in Atari Games

https://github.com/yukezhu/tensorflow-reinforce

Deep Reinforcement Learning with Double Q-learning

https://github.com/yun-kwak/decision-transformer-jax

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/yusme/DDPG

Prioritized Experience Replay

https://github.com/yuta0821/agent57_pytorch

Agent57: Outperforming the Atari Human Benchmark

https://github.com/yzheng51/rl-dino-run

Deep Reinforcement Learning with Double Q-learning

https://github.com/zanghyu/offline_bisimulation

MICo: Improved representations via sampling-based state similarity for Markov decision processes

https://github.com/zhengant/dqn_reversi

Deep Reinforcement Learning with Double Q-learning

https://github.com/zijianh4/CROP-leaderboard.github.io

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/zjlab-ammi/llm4rl

Proximal Policy Optimization Algorithms

https://github.com/zoeyuchao/mappo

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/zsz-hst/RL_single_chase_point

Proximal Policy Optimization Algorithms

https://github.com/zynk13/dueling-dqn-Reinforcement-learning

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/zzmtsvv/rl_task

Decision Transformer: Reinforcement Learning via Sequence Modeling

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.