https://github.com/toshikwa/discor.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Papers with Code 아카이브
Deep Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/toshikwa/slac.pytorch
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/toshikwa/soft-actor-critic.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/toybox-rs/Toybox
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning
https://github.com/toybox-rs/openai-baselines-envs
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning
https://github.com/tphanson/xupr-drl
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
https://github.com/traai/async-deep-rl
https://github.com/trackmania-rl/tmrl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/transedward/pytorch-hdqn
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
https://github.com/tristandeleu/pytorch-maml-rl
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/ttajmajer/morl-dv
Modular Multi-Objective Deep Reinforcement Learning with Decision Values
https://github.com/tuantran23012000/Recommendation-system
https://github.com/tuomaso/SC2LE-implementation
https://github.com/twni2016/f-IRL
Learning Robust Rewards with Adversarial Inverse Reinforcement Learning
https://github.com/typoverflow/OfflineRL-Lib
https://github.com/uber-common/deep-neuroevolution
https://github.com/uber-research/ape-x
https://github.com/uber-research/ga-world-models
https://github.com/ucl-dark/skillhack
https://github.com/ucl-exoplanets/ADC2023-baseline
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/ugo-nama-kun/DQN-chainer
https://github.com/uncharted-technologies/risk-and-uncertainty
Estimating Risk and Uncertainty in Deep Reinforcement Learning
https://github.com/unstablecursor/robotino_rl
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
https://github.com/uoe-agents/derl
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
https://github.com/uoe-agents/ksl
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
https://github.com/uoe-agents/revisiting-maddpg
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uos-plccn/gym-pbn
Deep Reinforcement Learning for Control of Probabilistic Boolean Networks
https://github.com/uribyul/py_ai_clinician
https://github.com/utarumo/RL_implementation
https://github.com/utiasDSL/gym-pybullet-drones
https://github.com/uvipen/Super-mario-bros-A3C-pytorch
https://github.com/vcharvet/project-rl
https://github.com/vectorinstitute/neuralkernelbandits
https://github.com/veds12/torch_rl_squared
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/vermashresth/sequential_social_dilemma_games
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
https://github.com/victorchan314/cs287_final_project
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.