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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/aricsvenz/AI_for_Drugs

ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

https://github.com/arjung128/image-paragraph-captioning

Self-critical Sequence Training for Image Captioning

https://github.com/arnomoonens/yarll

Continuous control with deep reinforcement learning

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

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/ars-ashuha/quantile-regression-dqn-pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/arunajit/drl

Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning

https://github.com/arunavkonwar/world-models

https://github.com/arushi12130/SafeOptionCritic

The Option-Critic Architecture

https://github.com/asarav/MIT-Deep-Traffic-Solution

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/ash3n/Meta-Gradients

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/ash3n/Meta-SGD

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/ash3n/Meta-SGD-TF

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/ashaaher/Reinforcement-Learning-Project

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ashtawy/deeptraffic

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/ashwin-M-D/DM-Gym

OR-Gym: A Reinforcement Learning Library for Operations Research Problems

https://github.com/asjad99/Reinforcement-Learning

Deep Q-learning from Demonstrations

https://github.com/asonabend/dtr_via_surrogate_loss

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation

https://github.com/asparius/barlowrl

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/aspk/Tensorflow_Deep_RL

Parameter Space Noise for Exploration

https://github.com/aspk/space_battle

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/astanway/gated-conv-nets

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/astooke/accel_rl

Accelerated Methods for Deep Reinforcement Learning

https://github.com/astooke/rlpyt/tree/master/rlpyt/ul

Decoupling Representation Learning from Reinforcement Learning

https://github.com/atgambardella/pytorch-es

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/atpcurr/atpcurr

Towards Finding Longer Proofs

https://github.com/attentionagent/attentionagent.github.io

Neuroevolution of Self-Interpretable Agents

https://github.com/attentionneuron/attentionneuron.github.io

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning

https://github.com/aurelio-amerio/concretedropout

Concrete Dropout

https://github.com/aus10powell/Automated-Health-Responses

Learning through Dialogue Interactions by Asking Questions

https://github.com/austinsilveria/Banana-Collection-DQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/autumnwu/aggressive-q-learning-with-ensembles

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/avalon-benchmark/avalon

Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds

https://github.com/avillemin/Minecraft-AI

Playing Atari with Deep Reinforcement Learning

https://github.com/avisingh599/cog

COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning

https://github.com/avivelor/UnityMachineLearningForProjectButterfly

Continuous control with deep reinforcement learning

https://github.com/avivnavon/nash-mtl

Gradient Surgery for Multi-Task Learning

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