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Atari Games

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평가표: 65
보존된 평가 기록: 2,519

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://github.com/RLforlife/VER

공식 코드

Revisiting Prioritized Experience Replay: A Value Perspective

https://github.com/SamanKhamesian/Hybrid-Self-Attention-NEAT

공식 코드

Hybrid Self-Attention NEAT: A novel evolutionary approach to improve the NEAT algorithm

https://github.com/Spiegeldondi/A-Hybrid-Quantum-Classical-Framework-for-Reinforcement-Learning-of-Atari-Games

공식 코드

A quantum-classical reinforcement learning model to play Atari games

https://github.com/Unity-Technologies/obstacle-tower-env

공식 코드

Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning

https://github.com/WendyShang/dqn_zoo

공식 코드

Reinforcement Learning with Latent Flow

https://github.com/WendyShang/flare

공식 코드

Reinforcement Learning with Latent Flow

https://github.com/WolfLo/optimist

공식 코드

Conservative Optimistic Policy Optimization via Multiple Importance Sampling

https://github.com/XiaoxiaoGuo/tensor4rl

공식 코드

Hybrid Reinforcement Learning with Expert State Sequences

https://github.com/YuhangSong/GTN

공식 코드

Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning

https://github.com/Yusufma03/CVRL

공식 코드

Contrastive Variational Reinforcement Learning for Complex Observations

https://github.com/Yusufma03/DPFRL

공식 코드

Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations

https://github.com/YyzHarry/SV-RL

공식 코드

Harnessing Structures for Value-Based Planning and Reinforcement Learning

https://github.com/ZhengyaoJiang/graphbackup

공식 코드

Graph Backup: Data Efficient Backup Exploiting Markovian Transitions

https://github.com/abhi460729/Action-Duration-with-Contextual-Bandits-for-Deep-Reinforcement-Learning-in-Dynamic-Environments

공식 코드

Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments

https://github.com/abhi460729/GATN

공식 코드

Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains

https://github.com/acstutts/ceqr-dqn

공식 코드

Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning

https://github.com/adaptive-rl/adarl-code

공식 코드

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

https://github.com/agakshat/spacefortress

공식 코드

Learning Time-Sensitive Strategies in Space Fortress

https://github.com/ai-secure/crop

공식 코드

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/aig-upf/hierarchical-IW

공식 코드

Hierarchical Width-Based Planning and Learning

https://github.com/aig-upf/pi-IW

공식 코드

Deep Policies for Width-Based Planning in Pixel Domains

https://github.com/airi-institute/rate

공식 코드

Recurrent Action Transformer with Memory

https://github.com/aj-correa/unraveling-the-rainbow

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Unraveling the Rainbow: can value-based methods schedule?

https://github.com/akansha-kalra/RLC2024-DDT-Reward-Learning

공식 코드

Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback?

https://github.com/akulen/alphagateau

공식 코드

Enhancing Chess Reinforcement Learning with Graph Representation

https://github.com/alienkevin/frogger

공식 코드

Frog Soup: Zero-Shot, In-Context, and Sample-Efficient Frogger Agents

https://github.com/amujika/open-ended-reinforcement-learning-with-neural-reward-functions

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Open-Ended Reinforcement Learning with Neural Reward Functions

https://github.com/ann-w/solving_drl_tasks_with_es_and_linear_policy_networks

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Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks

https://github.com/anonuser-532438/policyrankinganon

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Ranking Policy Decisions

https://github.com/aptx395/deep-spiking-q-networks

공식 코드

Human-Level Control through Directly-Trained Deep Spiking Q-Networks

https://github.com/armahmood/totd-rndmdp-experiments

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True Online Temporal-Difference Learning

https://github.com/arnokha/reusing_convolutions

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Reusing Convolutional Activations from Frame to Frame to Speed up Training and Inference

https://github.com/arushi12130/SafeOptionCritic

공식 코드

Safe Option-Critic: Learning Safety in the Option-Critic Architecture

https://github.com/ashfaq1701/dqn-atari

공식 코드

Learning To Play Atari Games Using Dueling Q-Learning and Hebbian Plasticity

https://github.com/aslanides/aixijs

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Count-Based Exploration in Feature Space for Reinforcement Learning

https://github.com/atavakol/action-hypergraph-networks

공식 코드

Learning to Represent Action Values as a Hypergraph on the Action Vertices

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