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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/facebookresearch/slbo

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

https://github.com/facebookresearch/torchbeast

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/fantianwen/laalaz13E

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

https://github.com/fantianwen/leela13_training

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

https://github.com/fapont/hackaton-hiparis-2021

Hindsight Experience Replay

https://github.com/farama-foundation/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/farama-foundation/metaworld

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/farama-foundation/microrts-py

Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS

https://github.com/farama-foundation/miniwob-plusplus

Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

https://github.com/farama-foundation/vizdoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/fastforwardlabs/learning-to-learn

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

https://github.com/fdcl-gwu/gym-rotor

Continuous control with deep reinforcement learning

https://github.com/feifeiobama/rewireneuron

Progressive Neural Networks

https://github.com/felipeescallon/openai-gym

https://github.com/felix-laumann/Bayesian_CNN_ContinualLearning

Weight Uncertainty in Neural Networks

https://github.com/felix0901/interconnect-routing-gym

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

https://github.com/fengsterooni/dql

Deep Reinforcement Learning with Double Q-learning

https://github.com/feruxhi/thoughts

Continuous control with deep reinforcement learning

https://github.com/feryal/craft-env

Modular Multitask Reinforcement Learning with Policy Sketches

https://github.com/fgitmichael/SVAEfromSLAC

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

https://github.com/fgitmichael/TestModeDisentangling

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

https://github.com/fhbzc/FishAgentSimulation

Continuous control with deep reinforcement learning

https://github.com/fiberleif/evolution-strategies

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/fiberleif/nc_ddpg

Continuous control with deep reinforcement learning

https://github.com/fictivename/cesor_ppo

Efficient Risk-Averse Reinforcement Learning

https://github.com/fictivename/dcd_mujoco

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

https://github.com/filipolszewski/prioritized-her

Energy-Based Hindsight Experience Prioritization

https://github.com/filippogiruzzi/deep_q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/fiorenza2/OffCon3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/flavioschneider/ml_papers_presentations

Continuous control with deep reinforcement learning

https://github.com/flexi-framework/tffb

Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations

https://github.com/floodsung/DDPG

Continuous control with deep reinforcement learning

https://github.com/floringogianu/atari-agents

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/flow-project/ray

Ray: A Distributed Framework for Emerging AI Applications

https://github.com/flymin/darts

DARTS: Differentiable Architecture Search

https://github.com/fmfn/BayesianOptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

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