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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/insultedbymaths/6.s890

공식 코드

Efficient and Scalable Deep Reinforcement Learning for Mean Field Control Games

https://github.com/intelai/cerl

공식 코드

Collaborative Evolutionary Reinforcement Learning

https://github.com/intelligent-control-lab/statewise_constrained_policy_optimization

공식 코드

State-wise Constrained Policy Optimization

https://github.com/intelligent-environments-lab/CityLearn

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CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management

https://github.com/internlm/oreal

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Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning

https://github.com/inventec-ai-center/carl-siggraph2020

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CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion

https://github.com/irenezhang30/marco

공식 코드

Centralized Model and Exploration Policy for Multi-Agent RL

https://github.com/irom-lab/Invariant-Policy-Optimization

공식 코드

Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning

https://github.com/isayev/ReLeaSE

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Deep Reinforcement Learning for De-Novo Drug Design

https://github.com/ischlag/Fast-Weight-Memory-public

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Learning Associative Inference Using Fast Weight Memory

https://github.com/ischubert/l2e

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Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics

https://github.com/iskandor/snd

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Self-supervised network distillation: an effective approach to exploration in sparse reward environments

https://github.com/ispc-lab/marl4ad

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Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

https://github.com/ita9naiwa/rlrd-aaai2022

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An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation

https://github.com/iwhwang/Fine-Grained-Causal-RL

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Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning

https://github.com/jacarvalho/nopg

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A Nonparametric Off-Policy Policy Gradient

https://github.com/jackblandin/fairness-comparison

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Fairness Through Counterfactual Utilities

https://github.com/jacklinkk/torchgrl

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Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments

https://github.com/jackory/rpbt

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Learning Diverse Risk Preferences in Population-based Self-play

https://github.com/jackyk02/distributed_rl_benchmarks

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Efficient Parallel Reinforcement Learning Framework using the Reactor Model

https://github.com/jacob-parnell-rozetta/longformer_coverage

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A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization

https://github.com/jacobandreas/nmn2

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Learning to Compose Neural Networks for Question Answering

https://github.com/jacobandreas/psketch

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Modular Multitask Reinforcement Learning with Policy Sketches

https://github.com/jacobfulano/learning-rules-with-bmi

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Distinguishing Learning Rules with Brain Machine Interfaces

https://github.com/jacobha/q-bounding-in-compositional-rl

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Bounding the Optimal Value Function in Compositional Reinforcement Learning

https://github.com/jacooba/hyper

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Hypernetworks in Meta-Reinforcement Learning

https://github.com/jaearly/mil-for-non-markovian-reward-modelling

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Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning

https://github.com/jaehyeon-son/dicp

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Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning

https://github.com/jaggbow/saf

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Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

https://github.com/jaimesabalimperial/jaisalab

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Distributional constrained reinforcement learning for supply chain optimization

https://github.com/jakefawkes/dr_distributional_test

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Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects

https://github.com/jakegrigsby/cc-afbc

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A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets

https://github.com/jakegrigsby/deep_control

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A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets

https://github.com/jakegrigsby/dmc_remastered

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Measuring Visual Generalization in Continuous Control from Pixels

https://github.com/jakegrigsby/super_sac

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A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets

https://github.com/jan-schuchardt/learning-to-evolve

공식 코드

Learning to Evolve

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