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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/danielwillemsen/PendulumDemo

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/danielytan/doom_DQN

A Deeper Look at Experience Replay

https://github.com/danifuertes/top_transformer

Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning

https://github.com/danilonumeroso/MEG

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/dannysdeng/dqn-pytorch

Distributed Prioritized Experience Replay

https://github.com/daochenzha/autoshard

Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning

https://github.com/darkrush/meta_learn

Continuous control with deep reinforcement learning

https://github.com/darshil333/CSE574

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

https://github.com/data-science-in-mechanical-engineering/hci-gibo

Simple random search provides a competitive approach to reinforcement learning

https://github.com/datamllab/rlcard-tutorial

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/david-abel/continuous_state_sa

Learning State Abstractions for Transfer in Continuous Control

https://github.com/daviddcho/supermario

Playing Atari with Deep Reinforcement Learning

https://github.com/davidsonic/robust-grasp

Robust Adversarial Reinforcement Learning

https://github.com/davidsonic/self_brewed_gym

https://github.com/davidzhou9/DeepVol

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

https://github.com/dchetelat/acer

Continuous control with deep reinforcement learning

https://github.com/debajit15kgp/multiagent-envs

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

https://github.com/debjitpaul/refiner

Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

https://github.com/deckardagent/deckard

Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling

https://github.com/deconlabs/Binanace-trading-simulation

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/deconlabs/Binanace_trading_simulation

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/deconlabs/TradingZoo-Dynamic-fee-simulation

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/deepcrypto/Reinforcement-learning-in-portfolio-management-

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/deepmind/acme

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/deepmind/acme/tree/master/acme/agents/jax/dqn

Munchausen Reinforcement Learning

https://github.com/deepmind/deepmind-research/tree/master/transporter

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/deepmind/enn_acme

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/deepmind/haiku

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

https://github.com/deepmind/rlax

Deep Reinforcement Learning with Double Q-learning

https://github.com/deepmind/tell_me_why_explanations_rl

Tell me why! Explanations support learning relational and causal structure

https://github.com/deepmipt/DeepPavlov

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

https://github.com/deepmipt/diff_beam_search/tree/master/expected_bleu

Differentiable lower bound for expected BLEU score

https://github.com/deepmipt/expected_bleu

Differentiable lower bound for expected BLEU score

https://github.com/deepsense-ai/carla-birdeye-view

Simulation-based reinforcement learning for real-world autonomous driving

https://github.com/denisewong1/ASX300

Reinforcement Learning for Market Making in a Multi-agent Dealer Market

https://github.com/denisyarats/pytorch_sac

Soft Actor-Critic Algorithms and Applications

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