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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/ethz-asl/Learn-to-Calibrate

공식 코드

Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning

https://github.com/ethz-asl/navrep

공식 코드

NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments

https://github.com/eugenevinitsky/robust_RL_multi_adversary

공식 코드

Robust Reinforcement Learning using Adversarial Populations

https://github.com/eugenevinitsky/sequential_social_dilemma_games

공식 코드

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

https://github.com/evalarzj/cped

공식 코드

Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement Learning

https://github.com/evergi/real_validation_saferl_treec_paper

공식 코드

Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems

https://github.com/evertes/distributional_SF

공식 코드

A neurally plausible model learns successor representations in partially observable environments

https://github.com/evgenii-nikishin/omd

공식 코드

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation

https://github.com/evgenii-nikishin/rl_with_resets

공식 코드

The Primacy Bias in Deep Reinforcement Learning

https://github.com/evgps/a3c_trading

공식 코드

Using Reinforcement Learning in the Algorithmic Trading Problem

https://github.com/extess0/darl

공식 코드

Dual-Agent Reinforcement Learning for Automated Feature Generation

https://github.com/ez-hwh/autoextraction

공식 코드

Adaptive Ordered Information Extraction with Deep Reinforcement Learning

https://github.com/ezliu/dream

공식 코드

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

https://github.com/fabcaio/microgrid_rl

공식 코드

Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids

https://github.com/fabien-couthouis/xai-in-rl

공식 코드

Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values

https://github.com/fabiopardo/tonic

공식 코드

Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking

https://github.com/fabriziocarpi/RLdecoding

공식 코드

Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

https://github.com/facebook/MazeBase

공식 코드

MazeBase: A Sandbox for Learning from Games

https://github.com/facebook/MemNN

공식 코드

Dialogue Learning With Human-In-The-Loop

https://github.com/facebookresearch/ELF

공식 코드

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

https://github.com/facebookresearch/Hanabi_SAD

공식 코드

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

https://github.com/facebookresearch/House3D

공식 코드

Building Generalizable Agents with a Realistic and Rich 3D Environment

https://github.com/facebookresearch/M3RL

공식 코드

M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning

https://github.com/facebookresearch/WhereDidMyOptimumGo

공식 코드

Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods

https://github.com/facebookresearch/active-mri-acquisition

공식 코드

Active MR k-space Sampling with Reinforcement Learning

https://github.com/facebookresearch/amortized-optimization-tutorial

공식 코드

Tutorial on amortized optimization

https://github.com/facebookresearch/benchmarl

공식 코드

BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

https://github.com/facebookresearch/controllable_agent

공식 코드

Does Zero-Shot Reinforcement Learning Exist?

https://github.com/facebookresearch/dcd

공식 코드

Learning Curricula in Open-Ended Worlds

https://github.com/facebookresearch/deep_bisim4control

공식 코드

Learning Invariant Representations for Reinforcement Learning without Reconstruction

https://github.com/facebookresearch/deeprl-continuing-tasks

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An Empirical Study of Deep Reinforcement Learning in Continuing Tasks

https://github.com/facebookresearch/drqv2

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Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/facebookresearch/fair_self_supervision_benchmark

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Scaling and Benchmarking Self-Supervised Visual Representation Learning

https://github.com/facebookresearch/gala

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Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

https://github.com/facebookresearch/gen_dgrl

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The Generalization Gap in Offline Reinforcement Learning

https://github.com/facebookresearch/habitat-api

공식 코드

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

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