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OpenAI Gym

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보존된 평가 기록: 45

An open-source toolkit from OpenAI that implements several Reinforcement Learning benchmarks including: classic control, Atari, Robotics and MuJoCo tasks.

(Description by Evolutionary learning of interpretable decision trees)

(Image Credit: OpenAI Gym)

코드

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

https://github.com/sirakik/ppo_football

Proximal Policy Optimization Algorithms

https://github.com/siyuofzhou/DDPG

Continuous control with deep reinforcement learning

https://github.com/sliao-mi-luku/DeepRL-multiple-agents-tennis-udacity-drlnd-p3

Continuous control with deep reinforcement learning

https://github.com/songrotek/DDPG

Continuous control with deep reinforcement learning

https://github.com/soumik12345/DDPG

Continuous control with deep reinforcement learning

https://github.com/soumik12345/Twin-Delayed-DDPG

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/stevenpjg/ddpg-aigym

Continuous control with deep reinforcement learning

https://github.com/sunfex/weighted-sac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/susan-amin/SparseBaseline1

Continuous control with deep reinforcement learning

https://github.com/synthlabsai/big-math

Proximal Policy Optimization Algorithms

https://github.com/taku-y/20181125-pybullet

Continuous control with deep reinforcement learning

https://github.com/takuseno/d3rlpy

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/takuseno/ppo

Proximal Policy Optimization Algorithms

https://github.com/tarod13/SAC

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tatsu-lab/linguistic_calibration

Proximal Policy Optimization Algorithms

https://github.com/tcmxx/CNTKUnityTools

Proximal Policy Optimization Algorithms

https://github.com/tegg89/DLCamp_Jeju2018

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnet

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnn

Continuous control with deep reinforcement learning

https://github.com/tegg89/mann

Continuous control with deep reinforcement learning

https://github.com/tencent-ailab/Arena

Arena: a toolkit for Multi-Agent Reinforcement Learning

https://github.com/tensorlayer/RLzoo

Continuous control with deep reinforcement learning

https://github.com/thainv0212/re-ddpg

Continuous control with deep reinforcement learning

https://github.com/theresearchai/vehicle_routing_rl_2

Proximal Policy Optimization Algorithms

https://github.com/thomashirtz/pytorch-soft-actor-critic

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/thomashirtz/soft-actor-critic

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tidedra/vl-rlhf

Proximal Policy Optimization Algorithms

https://github.com/tilkb/thermoai

Continuous control with deep reinforcement learning

https://github.com/timoklein/car_racer

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tjcdev/mlpgym

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/tju-drl-lab/self-supervised-rl

What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator

https://github.com/tliu1997/rnac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tmjeong1103/RL_with_RAY

Proximal Policy Optimization Algorithms

https://github.com/tobiasemrich/SchafkopfRL

Proximal Policy Optimization Algorithms

https://github.com/tomaszsmaruj25/Twin-Delayed-DDPG-Implementation

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/tommyvsfu1/RL-NTU

Proximal Policy Optimization Algorithms

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