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General Reinforcement Learning

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하위 태스크: 2
평가표: 6
보존된 평가 기록: 12

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/liziniu/ReMax

공식 코드

ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models

https://github.com/lucascjysdl/dgms-for-offline-policy-learning

공식 코드

Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions

https://github.com/lucascjysdl/offline-rl-kit

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Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

https://github.com/machelreid/can-wikipedia-help-offline-rl

공식 코드

Can Wikipedia Help Offline Reinforcement Learning?

https://github.com/mamba413/room

공식 코드

Robust Offline Reinforcement learning with Heavy-Tailed Rewards

https://github.com/manantomar/agent-centric-representations

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Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information

https://github.com/maohangyu/pdit

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PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning

https://github.com/maoliyuan/odice-pytorch

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ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update

https://github.com/maoyixiu/dmg

공식 코드

Doubly Mild Generalization for Offline Reinforcement Learning

https://github.com/marc-rigter/1r2r

공식 코드

One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning

https://github.com/marc-rigter/rambo

공식 코드

RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning

https://github.com/martius-lab/iCEM

공식 코드

Sample-efficient Cross-Entropy Method for Real-time Planning

https://github.com/martius-lab/ppgs

공식 코드

Planning from Pixels in Environments with Combinatorially Hard Search Spaces

https://github.com/martius-lab/puzzlegen

공식 코드

Planning from Pixels in Environments with Combinatorially Hard Search Spaces

https://github.com/mary-wu/smdp

공식 코드

Semi-Markov Offline Reinforcement Learning for Healthcare

https://github.com/matsuolab/BREMEN

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Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

https://github.com/matthewwicker/bnnreachavoid

공식 코드

Probabilistic Reach-Avoid for Bayesian Neural Networks

https://github.com/maximecb/gym-miniworld

공식 코드

DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs

https://github.com/mb-29/exploration

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FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems

https://github.com/mcgillmrl/robot_learning

공식 코드

Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning

https://github.com/menggedu/discover

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DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning

https://github.com/michaeltmatthews/jax2d

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Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks

https://github.com/michelllepan/neural-coprocessors

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Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation

https://github.com/microsoft/atac

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Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/microsoft/logrl

공식 코드

Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning

https://github.com/mig-zh/OODP

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Object-Oriented Dynamics Predictor

https://github.com/mila-iqia/conscious-planning

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A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

https://github.com/mila-iqia/skipper

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Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning

https://github.com/mila-iqia/spr

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Data-Efficient Reinforcement Learning with Self-Predictive Representations

https://github.com/ml-jku/OfflineRL

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A Dataset Perspective on Offline Reinforcement Learning

https://github.com/ml-jku/align-rudder

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Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

https://github.com/ml-postech/gm-vae

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Hyperbolic VAE via Latent Gaussian Distributions

https://github.com/ml-research/rational_activations

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Adaptive Rational Activations to Boost Deep Reinforcement Learning

https://github.com/ml-research/rational_rl

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Adaptive Rational Activations to Boost Deep Reinforcement Learning

https://github.com/ml-research/rational_sl

공식 코드

Adaptive Rational Activations to Boost Deep Reinforcement Learning

https://github.com/mld3/offlinerl_factoredactions

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Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.