Papers with Code 아카이브

Deep Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

코드

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

https://github.com/eleurent/highway-env

공식 코드

Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge

https://github.com/eleurent/rl-agents

공식 코드

Budgeted Reinforcement Learning in Continuous State Space

https://github.com/elharirymatteo/rans

공식 코드

DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories

https://github.com/eloialonso/iris

공식 코드

Transformers are Sample-Efficient World Models

https://github.com/elvisyjlin/lf2gym

공식 코드

Deep Reinforcement Learning for Playing 2.5D Fighting Games

https://github.com/emanuel-metzenthin/rl-scene-text-detection

공식 코드

Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

https://github.com/emily-halina/brisket

공식 코드

Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI

https://github.com/emunaran/stochastic-human-driving-policies-drl

공식 코드

Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars

https://github.com/epignatelli/navix

공식 코드

NAVIX: Scaling MiniGrid Environments with JAX

https://github.com/ercbunny/isaacgymenvs

공식 코드

Learning Generalizable Policy for Obstacle-Aware Autonomous Drone Racing

https://github.com/ercumentilhan/advice-imitation-reuse

공식 코드

Learning on a Budget via Teacher Imitation

https://github.com/ercumentilhan/advice-novelty

공식 코드

Student-Initiated Action Advising via Advice Novelty

https://github.com/ercumentilhan/naive-advice-imitation

공식 코드

Action Advising with Advice Imitation in Deep Reinforcement Learning

https://github.com/ericotjo001/srd

공식 코드

Self Reward Design with Fine-grained Interpretability

https://github.com/ericyangyu/pocar

공식 코드

Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems

https://github.com/ermongroup/PatchDrop

공식 코드

Learning When and Where to Zoom with Deep Reinforcement Learning

https://github.com/esraaelelimy/gtd_algos

공식 코드

Deep Reinforcement Learning with Gradient Eligibility Traces

https://github.com/ethancjackson/NoveltySearchLevenshtein

공식 코드

Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance

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/eugenevinitsky/sequential_social_dilemma_games

공식 코드

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

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

공식 코드

The Primacy Bias in Deep Reinforcement Learning

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

공식 코드

Adaptive Ordered Information Extraction with Deep Reinforcement Learning

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/facebookresearch/WhereDidMyOptimumGo

공식 코드

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

https://github.com/facebookresearch/dcd

공식 코드

Learning Curricula in Open-Ended Worlds

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

공식 코드

An Empirical Study of Deep Reinforcement Learning in Continuing Tasks

https://github.com/facebookresearch/drqv2

공식 코드

VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning

https://github.com/facebookresearch/gala

공식 코드

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

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

공식 코드

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

https://github.com/facebookresearch/how-to-autorl

공식 코드

Hyperparameters in Reinforcement Learning and How To Tune Them

https://github.com/facebookresearch/icp-block-mdp

공식 코드

Learning Domain Invariant Representations in Goal-conditioned Block MDPs

https://github.com/facebookresearch/level-replay

공식 코드

Prioritized Level Replay

https://github.com/facebookresearch/measuring-emergent-comm

공식 코드

On the Pitfalls of Measuring Emergent Communication

https://github.com/facebookresearch/minihack

공식 코드

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

https://github.com/facebookresearch/modem

공식 코드

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

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