https://github.com/shariqiqbal2810/maddpg-pytorch
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Papers with Code 아카이브
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/shariqiqbal2810/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/sharisun18/Absent_Supervisor_Env
https://github.com/shashankMadan-designEsthetics/ParlAICustom
https://github.com/shashist/recsys-rl
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
https://github.com/shashwatsaxena571/DRL-navigation
https://github.com/sheetalreddy/One-shot-learning
https://github.com/shehroze37/Augmented-Deep-Reinforcement-Learning-for-3D-environments
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
https://github.com/shehrum/RL_Continous-Control
https://github.com/shehrum/RL_Navigation
https://github.com/sherry4186/Distributed-DQN
https://github.com/sheryl-ai/MetaPred
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/shibhansh/loss-of-plasticity
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness
https://github.com/shinian123/gym
https://github.com/sholtodouglas/pointMass
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
https://github.com/shreyasc-13/off_domain_rlkit
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
https://github.com/shriram2112/Reinforcement_learning_gym
https://github.com/shuli0808/DQN
https://github.com/shunzh/pytorch-maml-rl
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/shyamal-anadkat/offlinerl
https://github.com/shyamsn97/controllable-ncas
HyperNCA: Growing Developmental Networks with Neural Cellular Automata
https://github.com/shyamsn97/hyper-nn
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
https://github.com/sibeshkar/gym
https://github.com/siddharthnishtala/DQN-Trader
Deep reinforcement learning for time series: playing idealized trading games
https://github.com/siddikui/Fast-DARTS
https://github.com/sidney1505/arc_maml_transformer
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/silvadenisaraujo/OpenAIGym-Taxi-v2
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
https://github.com/silvialuu/DRL-2018
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
https://github.com/simoll/apo
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/simsal0r/mixture-of-decision-trees
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
https://github.com/simsimiSION/pymarl-algorithm-extension-via-starcraft
https://github.com/sinaghiassian/OffpolicyAlgorithms
https://github.com/sinamoradian/SNIP-NAS-2
https://github.com/sisl/gail-driver
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sisl/ngsim_env
https://github.com/siyuofzhou/DDPG
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.