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

https://github.com/nvlabs/gbrl_sb3

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/nvlabs/long-video-gan

Towards Accurate Generative Models of Video: A New Metric & Challenges

https://github.com/nvshrao/Elastic-Weight-Consolidation

Overcoming catastrophic forgetting in neural networks

https://github.com/odokumaci/rainbow-unity-obstacle-tower-challenge

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/ofekluis/sonic_project_ss19

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/olonok69/Udacity_Banana_Unity

Prioritized Experience Replay

https://github.com/omkarv/pong-from-pixels

Playing Atari with Deep Reinforcement Learning

https://github.com/ondrejba/action_attention_iclr_22

Contrastive Learning of Structured World Models

https://github.com/openai/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/openai/universe-starter-agent

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/opendilab/DI-engine

Deep Reinforcement Learning with Double Q-learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/c51.py

A Distributional Perspective on Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/collaq.py

Multi-Agent Collaboration via Reward Attribution Decomposition

https://github.com/opendilab/DI-engine/blob/main/ding/policy/coma.py

Counterfactual Multi-Agent Policy Gradients

https://github.com/opendilab/DI-engine/blob/main/ding/policy/impala.py

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

https://github.com/opendilab/DI-engine/blob/main/ding/policy/iqn.py

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qmix.py

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

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qrdqn.py

Distributional Reinforcement Learning with Quantile Regression

https://github.com/opendilab/DI-engine/blob/main/ding/policy/r2d2.py

https://github.com/opendilab/DI-engine/blob/main/ding/policy/rainbow.py

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/rnd_reward_model.py

Exploration by Random Network Distillation

https://github.com/opendilab/LightZero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/openpsi-projects/srl

Proximal Policy Optimization Algorithms

https://github.com/openrlbenchmark/openrlbenchmark

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

https://github.com/opplieam/Pong-Deep-RL

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/osilab-kaist/smac_exp

The StarCraft Multi-Agent Challenge

https://github.com/osilab-kaist/smac_plus

The StarCraft Multi-Agent Challenge

https://github.com/oxwhirl/comix

FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/oxwhirl/facmac

The StarCraft Multi-Agent Challenge

https://github.com/paintception/Deep-Quality-Value-DQV-Learning-

Deep Quality-Value (DQV) Learning

https://github.com/paintception/Deep-Quality-Value-Family

Playing Atari with Deep Reinforcement Learning

https://github.com/paintception/Deep-Quality-Value-Family-

Playing Atari with Deep Reinforcement Learning

https://github.com/parilo/gym_bipedal_walker_v2_solution

A Distributional Perspective on Reinforcement Learning

https://github.com/parilo/rl-server

Playing Atari with Deep Reinforcement Learning

https://github.com/pat-coady/trpo

Trust Region Policy Optimization

https://github.com/pathway/alphaxos

Deep Reinforcement Learning with Double Q-learning

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