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Reinforcement Learning

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코드

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

https://github.com/alathiya/RL-Quadcoptor-Flying

Continuous control with deep reinforcement learning

https://github.com/alec-tschantz/vime

VIME: Variational Information Maximizing Exploration

https://github.com/alecfilios/Training-Intelligent-game-Agents-through-Competitive-Reinforcement-Learning

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/alescontrela/viper

Mastering Diverse Domains through World Models

https://github.com/alessandrositta/Flatland_challenge

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/alexandonian/pretorched-x

Progressive Neural Architecture Search

https://github.com/alexbaumi/PPO-Algorithm

Proximal Policy Optimization Algorithms

https://github.com/alexbaumi/PPO-Algorithms

Proximal Policy Optimization Algorithms

https://github.com/alexcercos/ML-Agents

Unity: A General Platform for Intelligent Agents

https://github.com/alexdominguez09/pedestrian_direction

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/alexeypustynnikov/AM-VRP

A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems

https://github.com/alexfrom0815/Online-3D-BPP-PCT

Learning Practically Feasible Policies for Online 3D Bin Packing

https://github.com/alexis-jacq/LOLA

Learning with Opponent-Learning Awareness

https://github.com/alexis-jacq/LOLA_DICE

Learning with Opponent-Learning Awareness

https://github.com/alexis-jacq/numpy_ARS

Simple random search provides a competitive approach to reinforcement learning

https://github.com/alexlee-gk/slac

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/alexmlamb/blocks_rl_gru_setup

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/alexsax/testing_rlpyt

Accelerated Methods for Deep Reinforcement Learning

https://github.com/alextrudeau/go_exploit

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/alexub/jax-meta-learning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/alhabk/SGEE--pytorch

Continuous control with deep reinforcement learning

https://github.com/aliansgp/RL-SAC-CARLA

Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning

https://github.com/alielhassouni/heartsteps-gaussian-generative-model

Group-driven Reinforcement Learning for Personalized mHealth Intervention

https://github.com/aliengirlliv/teachable

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/alirezakazemipour/DIAYN-PyTorch

Diversity is All You Need: Learning Skills without a Reward Function

https://github.com/alirezakazemipour/ppo-rnd

Proximal Policy Optimization Algorithms

https://github.com/alisidd/Evolution-Strategies

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/allanpichardo/dontpanic

Accuracy-based Curriculum Learning in Deep Reinforcement Learning

https://github.com/allenai/allenact

AllenAct: A Framework for Embodied AI Research

https://github.com/allenai/robothor-challenge

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/alpaficia/MOSEAC_Limo

Soft Actor-Critic Algorithms and Applications

https://github.com/alpha-vllm/llama2-accessory

LIMA: Less Is More for Alignment

https://github.com/alphadl/darts.pytorch1.1

DARTS: Differentiable Architecture Search

https://github.com/alreadydone/Leela_Phoenix

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/alt113/flow-triangle-scenario

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/aluscher/torchbeastpopart

Multi-task Deep Reinforcement Learning with PopArt

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