Papers with Code 아카이브

Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/starry-sky6688/MADDPG

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/starry-sky6688/marl-algorithms

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/stavrosgreece/MultiAgentLearning

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/stefanowoerner/medimeta-pytorch

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

https://github.com/stephen-chung-mh/map_prop

MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents

https://github.com/stevenpjg/ddpg-aigym

Continuous control with deep reinforcement learning

https://github.com/steventango/jumpstart-rl

Jump-Start Reinforcement Learning

https://github.com/stevep2007/https-github.com-ZhengyaoJiang-PGPortfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/stevezhang1990/dma_rl

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

https://github.com/stewartyoung/DeepRL-PM

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/stratismarkou/sample-efficient-bayesian-rl

Posterior Sampling for Reinforcement Learning Without Episodes

https://github.com/subhadip-maiti/tinydqn

Playing Atari with Deep Reinforcement Learning

https://github.com/subinlab/model_based_rl_paper

Temporal Difference Variational Auto-Encoder

https://github.com/sudh1976/ECE510_DeepLearning_Project

An Alternative Softmax Operator for Reinforcement Learning

https://github.com/sufengniu/GVIN

Value Iteration Networks

https://github.com/suhoy901/SeqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/sujoung/debuggedDDQ

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

https://github.com/sukiboo/personalization_wain21

On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks

https://github.com/sumitsk/HER

Hindsight Experience Replay

https://github.com/sumkumar/hiro_impl

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/sundave1998/independent-npg-mpg

Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning

https://github.com/sunfex/weighted-sac

Prioritized Experience Replay

https://github.com/sunjeet95/Deep-Q-Network-using-Tensorflow

Playing Atari with Deep Reinforcement Learning

https://github.com/sunnyyzj/morl-baselines

A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

https://github.com/sunyasheng/a2rl_pytorch

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

https://github.com/super5-22/habitat-vis

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

https://github.com/suragnair/alpha-zero-general

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

https://github.com/suragnair/seqGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/sureli/rrls

Robust Adversarial Reinforcement Learning

https://github.com/suryakmurthy/uam_noise_aware_agent

A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance

https://github.com/suryakmurthy/uam_shared_scheduling

A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance

https://github.com/susan-amin/SparseBaseline1

Continuous control with deep reinforcement learning

https://github.com/susanwe/ope_worldmodels

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/susumuota/distributed_experience_replay

RLlib: Abstractions for Distributed Reinforcement Learning

https://github.com/susumuota/gym-modeestimation

Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning

https://github.com/susumuota/gym-oculoenv

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

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