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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/deniztekalp/comp-491-bitirme
Beating Atari with Natural Language Guided Reinforcement Learning
https://github.com/dennysemko/VectorizedBayesByBackprop
https://github.com/desire2020/RankGAN
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/developer-smartwebzone/-reinforcement-learning-algorithms.
https://github.com/dfki-ric-underactuated-lab/torque_limited_simple_pendulum
https://github.com/dgabsi/goal_exploration_amigo
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
https://github.com/dhruvramani/Transformers-RL
https://github.com/dickreuter/neuron_poker
https://github.com/diff7/DARTS-devices
https://github.com/dimvlachogiannis/humanlight
https://github.com/dirtdust/TC-Bot-python3
https://github.com/div99/xql
https://github.com/dkalpakchi/ReproducingSCAPytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/dkangin/baselines/tree/master/baselines/trpo_replay
On-Policy Trust Region Policy Optimisation with Replay Buffers
https://github.com/dlasecki/QaoaParamsPredictorApi
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
https://github.com/dllllb/neurips2021-nethack-raph
https://github.com/dmeoli/neuro-sat
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
https://github.com/dmiu-shell/deeprl-shell
https://github.com/dmksjfl/Job_Shop_Scheduling_Problem_with_Reinforcement_Learning
A Reinforcement Learning Environment For Job-Shop Scheduling
https://github.com/dmksjfl/smr
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse
https://github.com/dmlc/dgl/tree/master/examples/pytorch/caregnn
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
https://github.com/dmund95/Off-Policy-Deep-RL
https://github.com/dobro12/CPO
https://github.com/domingoesteban/hiu_sac
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies
https://github.com/donamin/llc
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/dongchen06/macacc
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/dongjae-kim/simul-mdps
On the Reliability and Generalizability of Brain-inspired Reinforcement Learning Algorithms
https://github.com/doty-k/world_models
https://github.com/doubtedsteam/das
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
https://github.com/dpaleka/cross-entropy-for-combinatorics
https://github.com/dpoulopoulos/drl_collaborate_compete
https://github.com/dpoulopoulos/drl_continuous_control
https://github.com/dragen1860/DARTS-PyTorch
https://github.com/dragen1860/MAML-Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/dragen1860/MAML-Pytorch-RL
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.