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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/hzn666/rlbid_ea

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Real-time Bidding Strategy in Display Advertising: An Empirical Analysis

https://github.com/hzwer/ICCV2019-LearningToPaint

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Learning to Paint With Model-based Deep Reinforcement Learning

https://github.com/i22024/lvlm2p

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Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation

https://github.com/iDurugkar/adversarial-intrinsic-motivation

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Adversarial Intrinsic Motivation for Reinforcement Learning

https://github.com/iancovert/dynamic-selection

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Learning to Maximize Mutual Information for Dynamic Feature Selection

https://github.com/iasawseen/MultiServerRL

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Artificial Intelligence for Prosthetics - challenge solutions

https://github.com/ibm/atari-active-learning

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Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari

https://github.com/ibm/loa

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LOA: Logical Optimal Actions for Text-based Interaction Games

https://github.com/ibm/starling-agent

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STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models

https://github.com/ibm/swatgym

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A SWAT-based Reinforcement Learning Framework for Crop Management

https://github.com/icaros-usc/dqd-rl

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Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning

https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning

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Action Robust Reinforcement Learning and Applications in Continuous Control

https://github.com/icu-sepsis/icu-sepsis

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ICU-Sepsis: A Benchmark MDP Built from Real Medical Data

https://github.com/idanshen/Value-Augmented-Sampling

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Value Augmented Sampling for Language Model Alignment and Personalization

https://github.com/idea-iitd/GCOMB

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Learning Heuristics over Large Graphs via Deep Reinforcement Learning

https://github.com/ido90/CeSoR

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Efficient Risk-Averse Reinforcement Learning

https://github.com/ido90/Rewards-Deterioration-Detection

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Detecting Rewards Deterioration in Episodic Reinforcement Learning

https://github.com/idomatan/robofriend

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Robofriend: An Adpative Storytelling Robotic Teddy Bear - Technical Report

https://github.com/idsia/gogepo

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Goal-Conditioned Generators of Deep Policies

https://github.com/idsia/policyevaluator

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General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States

https://github.com/igilitschenski/multi_car_racing

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Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space

https://github.com/iglu-contest/gridworld

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IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

https://github.com/ignc-research/arena-rosnav

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Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems

https://github.com/ignc-research/ir-drl

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Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation

https://github.com/ignc-research/navsafe-arena

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Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation

https://github.com/ihholmes-p/ares

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Attention-Based Reward Shaping for Sparse and Delayed Rewards

https://github.com/ihuhuhu/RAC

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Balancing Value Underestimation and Overestimation with Realistic Actor-Critic

https://github.com/iitis/RL_for_VQSD_ansatz_optimization

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Enhancing variational quantum state diagonalization using reinforcement learning techniques

https://github.com/iitkcpslab/rlstl

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STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning

https://github.com/ikostrikov/implicit_q_learning

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Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/ikostrikov/rlpd

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Efficient Online Reinforcement Learning with Offline Data

https://github.com/ikostrikov/walk_in_the_park

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A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

https://github.com/ilevyfan/meta_rl_scaling

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A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

https://github.com/ilgeehong/think-rm

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Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models

https://github.com/illidanlab/Simulator

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Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management

https://github.com/illidanlab/cdrl

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Collaborative Deep Reinforcement Learning

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