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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/eugenevinitsky/sequential_social_dilemma_games

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Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

https://github.com/evalarzj/cped

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Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement Learning

https://github.com/evergi/treec-paper-results

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TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm

https://github.com/evertes/distributional_SF

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A neurally plausible model learns successor representations in partially observable environments

https://github.com/evgenii-nikishin/omd

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Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation

https://github.com/evgenii-nikishin/rl_with_resets

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The Primacy Bias in Deep Reinforcement Learning

https://github.com/evgps/a3c_trading

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Using Reinforcement Learning in the Algorithmic Trading Problem

https://github.com/evolvinglmms-lab/mgpo

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High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning

https://github.com/exalearn/covid-drug-design

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Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates

https://github.com/existentialrobotics/cross_embodiment_transfer

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Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignment

https://github.com/eyalseg/ecrl

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Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution

https://github.com/ez-hwh/autoextraction

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Adaptive Ordered Information Extraction with Deep Reinforcement Learning

https://github.com/ezhang7423/language-control-diffusion

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Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks

https://github.com/ezliu/dream

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Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

https://github.com/faaizt/mr-ope

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Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits

https://github.com/fabien-couthouis/xai-in-rl

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Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values

https://github.com/fabiopardo/tonic

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Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking

https://github.com/fabrahman/Emo-Aware-Storytelling

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Modeling Protagonist Emotions for Emotion-Aware Storytelling

https://github.com/fabriziocarpi/RLdecoding

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Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

https://github.com/facebook/MazeBase

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MazeBase: A Sandbox for Learning from Games

https://github.com/facebook/MemNN

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Dialogue Learning With Human-In-The-Loop

https://github.com/facebookresearch/ELF

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ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

https://github.com/facebookresearch/Hanabi_SAD

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Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

https://github.com/facebookresearch/M3RL

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M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning

https://github.com/facebookresearch/active-mri-acquisition

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Active MR k-space Sampling with Reinforcement Learning

https://github.com/facebookresearch/adversarially-motivated-intrinsic-goals

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Learning with AMIGo: Adversarially Motivated Intrinsic Goals

https://github.com/facebookresearch/amortized-optimization-tutorial

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Tutorial on amortized optimization

https://github.com/facebookresearch/benchmarl

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BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

https://github.com/facebookresearch/controllable_agent

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Does Zero-Shot Reinforcement Learning Exist?

https://github.com/facebookresearch/dcd

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Evolving Curricula with Regret-Based Environment Design

https://github.com/facebookresearch/deep_bisim4control

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Learning Invariant Representations for Reinforcement Learning without Reconstruction

https://github.com/facebookresearch/deeprl-continuing-tasks

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An Empirical Study of Deep Reinforcement Learning in Continuing Tasks

https://github.com/facebookresearch/denoised_mdp

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Denoised MDPs: Learning World Models Better Than the World Itself

https://github.com/facebookresearch/drqv2

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Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/facebookresearch/e3b

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Exploration via Elliptical Episodic Bonuses

https://github.com/facebookresearch/explore

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Accelerating Exploration with Unlabeled Prior Data

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