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Atari Games

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평가표: 65
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The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

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

https://bitbucket.org/catk1ns0n/repr_public

공식 코드

Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting

https://github.com/0x1DA9430/Decision-Transformer-vs-Decision-Mamba

공식 코드

Decision Transformer vs. Decision Mamba: Analysing the Complexity of Sequential Decision Making in Atari Games

https://github.com/5vision/DARQN

공식 코드

Deep Attention Recurrent Q-Network

https://github.com/AdaCompNUS/diner-dash-simulator

공식 코드

DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space

https://github.com/AmazingAng/VER

공식 코드

Revisiting Prioritized Experience Replay: A Value Perspective

https://github.com/AndssY/DeMa

공식 코드

Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

https://github.com/BenedictWilkinsAI/S3N

공식 코드

A Metric Learning Approach to Anomaly Detection in Video Games

https://github.com/Benjamin-eecs/Theoretical-GMRL

공식 코드

A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

https://github.com/DSobscure/cgi_drl_platform

공식 코드

An Unsupervised Video Game Playstyle Metric via State Discretization

https://github.com/Div99/IQ-Learn

공식 코드

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/FragileTheory/FractalAI

공식 코드

Fractal AI: A fragile theory of intelligence

https://github.com/Francesco-Sovrano/Combining--experience-replay--with--exploration-by-random-network-distillation-

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Combining Experience Replay with Exploration by Random Network Distillation

https://github.com/GonQue/gbac

공식 코드

Learning to Perceive in Deep Model-Free Reinforcement Learning

https://github.com/HTRPOCODES/HTRPO-v2

공식 코드

Hindsight Trust Region Policy Optimization

https://github.com/Hananel-Hazan/bindsnet

공식 코드

Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games

https://github.com/HjalmarWijk/bounded-prescience

공식 코드

Shielding Atari Games with Bounded Prescience

https://github.com/HuTobias/HIGHLIGHTS-LRP

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Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

https://github.com/Hwhitetooth/lirpg

공식 코드

On Learning Intrinsic Rewards for Policy Gradient Methods

https://github.com/Hwhitetooth/random_gvfs

공식 코드

Learning State Representations from Random Deep Action-conditional Predictions

https://github.com/IDSIA/recurrent-fwp

공식 코드

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/IouJenLiu/HTS-RL

공식 코드

High-Throughput Synchronous Deep RL

https://github.com/JohanSamir/revisiting_rainbow

공식 코드

Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

https://github.com/KDL-umass/saliency_maps

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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning

https://github.com/KohlerHECTOR/interpreter-py

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Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning

https://github.com/Lww007/Text-Atari-Agents

공식 코드

TextAtari: 100K Frames Game Playing with Language Agents

https://github.com/MichaelAllen1966/qambo

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Developing an OpenAI Gym-compatible framework and simulation environment for testing Deep Reinforcement Learning agents solving the Ambulance Location Problem

https://github.com/MineDojo/MineDojo

공식 코드

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

https://github.com/MishaLaskin/curl

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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/MouseHu/GEM

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Generalizable Episodic Memory for Deep Reinforcement Learning

https://github.com/NUS-LID/RENAULT

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Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning

https://github.com/NVLABs/cule

공식 코드

Accelerating Reinforcement Learning through GPU Atari Emulation

https://github.com/NelsonAU/ale_countstates

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Estimates for the Branching Factors of Atari Games

https://github.com/NeteaseFuxiRL/action-balance-exploration

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Exploring Unknown States with Action Balance

https://github.com/NinaMaz/NAS_RL_torch

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Optimizing the Neural Architecture of Reinforcement Learning Agents

https://github.com/OscarHuangWind/Preference-Guided-DQN-Atari

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Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration

https://github.com/PatrykChrabaszcz/Canonical_ES_Atari

공식 코드

Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari

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