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OpenAI Gym

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하위 태스크: 1
평가표: 17
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/deconlabs/Binanace-trading-simulation

Proximal Policy Optimization Algorithms

https://github.com/deconlabs/Binanace_trading_simulation

Proximal Policy Optimization Algorithms

https://github.com/deconlabs/TradingZoo-Dynamic-fee-simulation

Proximal Policy Optimization Algorithms

https://github.com/denizmguen/IANNWTF2019-Project

Continuous control with deep reinforcement learning

https://github.com/deniztekalp/comp-491-bitirme

Beating Atari with Natural Language Guided Reinforcement Learning

https://github.com/developer-smartwebzone/-reinforcement-learning-algorithms.

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

https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum

Continuous control with deep reinforcement learning

https://github.com/dickreuter/neuron_poker

Proximal Policy Optimization Algorithms

https://github.com/dmiu-shell/deeprl-shell

Proximal Policy Optimization Algorithms

https://github.com/donamin/llc

Proximal Policy Optimization Algorithms

https://github.com/downingbots/RLDonkeycar

Proximal Policy Optimization Algorithms

https://github.com/dpoulopoulos/drl_collaborate_compete

Continuous control with deep reinforcement learning

https://github.com/dpoulopoulos/drl_continuous_control

Continuous control with deep reinforcement learning

https://github.com/ds4dm/learn2branch

Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

https://github.com/dyabel/handson_rl

Proximal Policy Optimization Algorithms

https://github.com/dyth/doublegum

Continuous control with deep reinforcement learning

https://github.com/eladsar/rbi

Proximal Policy Optimization Algorithms

https://github.com/electronicarts/interactive_training

Towards Interactive Training of Non-Player Characters in Video Games

https://github.com/emerge-lab/nocturne_lab

Proximal Policy Optimization Algorithms

https://github.com/evieq01/oodil

Proximal Policy Optimization Algorithms

https://github.com/facebookresearch/Horizon

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/ReAgent

Proximal Policy Optimization Algorithms

https://github.com/facebookresearch/rl/blob/main/examples/ddpg/ddpg.py

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/rl/blob/main/examples/sac/sac.py

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

https://github.com/facebookresearch/salina

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/facebookresearch/torchbeast

TorchBeast: A PyTorch Platform for Distributed RL

https://github.com/fdcl-gwu/gym-rotor

Continuous control with deep reinforcement learning

https://github.com/feruxhi/thoughts

Continuous control with deep reinforcement learning

https://github.com/fhbzc/FishAgentSimulation

Continuous control with deep reinforcement learning

https://github.com/fiberleif/nc_ddpg

Continuous control with deep reinforcement learning

https://github.com/fiorenza2/OffCon3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/flavioschneider/ml_papers_presentations

Continuous control with deep reinforcement learning

https://github.com/floodsung/DDPG

Continuous control with deep reinforcement learning

https://github.com/flowersteam/curious

Continuous control with deep reinforcement learning

https://github.com/flowersteam/rl_stats

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

https://github.com/fomorians-oss/awr

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

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