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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/MiuLab/TC-Bot

공식 코드

End-to-End Task-Completion Neural Dialogue Systems

https://github.com/MiuLab/UserSimulator

공식 코드

A User Simulator for Task-Completion Dialogues

https://github.com/Mohammadamin-Barekatain/multipolar

공식 코드

MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics

https://github.com/MoniFarsang/ppo-clipping-approaches

공식 코드

Decaying Clipping Range in Proximal Policy Optimization

https://github.com/MouseHu/GEM

공식 코드

Generalizable Episodic Memory for Deep Reinforcement Learning

https://github.com/MrtnMndt/meta-learning-CODEBRIM

공식 코드

Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset

https://github.com/MuhangTian/Fair-MORL-AAMAS

공식 코드

Welfare and Fairness in Multi-objective Reinforcement Learning

https://github.com/Multi-Agent-Networks/NaRLA

공식 코드

Giving Up Control: Neurons as Reinforcement Learning Agents

https://github.com/MushroomRL/mushroom-rl

공식 코드

MushroomRL: Simplifying Reinforcement Learning Research

https://github.com/MyCaffe/MyCaffe

공식 코드

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

https://github.com/NMerlis/TabulaRL

공식 코드

Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies

https://github.com/NSAPH-Projects/heat-alerts_RL

공식 코드

Optimizing Heat Alert Issuance with Reinforcement Learning

https://github.com/NUS-LID/RENAULT

공식 코드

Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning

https://github.com/NVIDIA-Omniverse/IsaacGymEnvs

공식 코드

DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality

https://github.com/NVIDIA-Omniverse/Orbit

공식 코드

Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments

https://github.com/NVIDIA/GraphQSat

공식 코드

Can $Q$-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

https://github.com/NVLABs/cule

공식 코드

Accelerating Reinforcement Learning through GPU Atari Emulation

https://github.com/NVlabs/DiffRL

공식 코드

Accelerated Policy Learning with Parallel Differentiable Simulation

https://github.com/NVlabs/GA3C

공식 코드

Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

https://github.com/NVlabs/unas

공식 코드

UNAS: Differentiable Architecture Search Meets Reinforcement Learning

https://github.com/NagisaZj/MetaCURE-Public

공식 코드

MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration

https://github.com/Nardien/NMG

공식 코드

Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation

https://github.com/NeteaseFuxiRL/action-balance-exploration

공식 코드

Exploring Unknown States with Action Balance

https://github.com/Networks-Learning/nevae

공식 코드

NeVAE: A Deep Generative Model for Molecular Graphs

https://github.com/Networks-Learning/tpprl

공식 코드

Deep Reinforcement Learning of Marked Temporal Point Processes

https://github.com/NeuroCSUT/DeepMind-Atari-Deep-Q-Learner-2Player

공식 코드

Multiagent Cooperation and Competition with Deep Reinforcement Learning

https://github.com/NeuroCSUT/intentions

공식 코드

Do deep reinforcement learning agents model intentions?

https://github.com/NevVerVer/DBS-Gym

공식 코드

Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson Disease

https://github.com/NiMlr/High-Dim-ES-RL

공식 코드

Challenges in High-dimensional Reinforcement Learning with Evolution Strategies

https://github.com/Nick-Kou/RL-PGO

공식 코드

RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization

https://github.com/NicolasAG/convai

공식 코드

The RLLChatbot: a solution to the ConvAI challenge

https://github.com/Nikunj-Gupta/dmcg-marl

공식 코드

Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning

https://github.com/NinaMaz/NAS_RL_torch

공식 코드

Optimizing the Neural Architecture of Reinforcement Learning Agents

https://github.com/Nirnai/DeepRL

공식 코드

How to Make Deep RL Work in Practice

https://github.com/NishanthVAnand/prediction_and_control_in_continual_reinforcement_learning

공식 코드

Prediction and Control in Continual Reinforcement Learning

https://github.com/Noahs-ARK/SPIGOT

공식 코드

Backpropagating through Structured Argmax using a SPIGOT

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