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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

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/b-faye/rpo

공식 코드

Value-Free Policy Optimization via Reward Partitioning

https://github.com/bach1292/KDD_Cup_2019_LOLS_Team

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Policy Learning for Malaria Control

https://github.com/badger-rl/rope

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State-Action Similarity-Based Representations for Off-Policy Evaluation

https://github.com/badger-rl/stop

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Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces

https://github.com/badger-rl/understandingdataaugmentationforrl

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Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates

https://github.com/baicenxiao/arel

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Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning

https://github.com/baicenxiao/shaping-advice

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Shaping Advice in Deep Reinforcement Learning

https://github.com/baichenjia/contrastive-ucb

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Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning

https://github.com/baichenjia/pbrl

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Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

https://github.com/baichenjia/utds

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Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning

https://github.com/baidu-research/DT-RAM

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Dynamic Computational Time for Visual Attention

https://github.com/baimingc/dambrl

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Delay-Aware Model-Based Reinforcement Learning for Continuous Control

https://github.com/baimingc/delay-aware-MARL

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Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments

https://github.com/baitingzbt/peda

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Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL

https://github.com/bakalex/autohedger

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Market-making with reinforcement-learning (SAC)

https://github.com/bancaditalia/ABCredit.jl

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Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling

https://github.com/bancaditalia/black-it/blob/main/examples/RL_to_combine_search_methods.ipynb

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Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs

https://github.com/bangyou01/pytorch-cody

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Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images

https://github.com/baoguangsheng/bart-rewriters

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A General Contextualized Rewriting Framework for Text Summarization

https://github.com/baoguangsheng/ctx-rewriter-for-summ

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Contextualized Rewriting for Text Summarization

https://github.com/baoqianwang/iros22_darl1n

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Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler Mitigation

https://github.com/barakeel/synthesis_datasets

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Deep Reinforcement Learning for Synthesizing Functions in Higher-Order Logic

https://github.com/barakgahtan/5gire

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5G Routing Interfered Environment

https://github.com/baranwa2/delayresolvedrl

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Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays

https://github.com/baratilab/meshdqn

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MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics

https://github.com/bark-simulator/bark-ml/tree/master/experiments

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Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments

https://github.com/bartbussmann/EmpathicDQN

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Towards Empathic Deep Q-Learning

https://github.com/bartekcupial/finetuning-rl-as-cl

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Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem

https://github.com/bassamlab/sigmarl

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SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning

https://github.com/battlewen/ro2o

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Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness

https://github.com/baturaysaglam/ac-off-poc

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Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach

https://github.com/baturaysaglam/dase

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Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms

https://github.com/baturaysaglam/discover

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Deep Intrinsically Motivated Exploration in Continuous Control

https://github.com/baturaysaglam/la3p

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Actor Prioritized Experience Replay

https://github.com/baturaysaglam/swtd3

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Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy Gradients

https://github.com/baudm/sungka-ai

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Mastering the Game of Sungka from Random Play

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