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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://github.com/neurips2019submission/Formal-Language-Constraints-for-Markov-Decision-Processes

공식 코드

Formal Language Constraints for Markov Decision Processes

https://github.com/nikonikolov/rltf

공식 코드

Information-Directed Exploration for Deep Reinforcement Learning

https://github.com/nvlabs/bcts

공식 코드

Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction

https://github.com/openai/baselines

공식 코드

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/openai/evolution-strategies-starter

공식 코드

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/openai/large-scale-curiosity

공식 코드

Large-Scale Study of Curiosity-Driven Learning

https://github.com/openai/random-network-distillation

공식 코드

Exploration by Random Network Distillation

https://github.com/oxwhirl/treeqn

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TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

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Soft Actor-Critic for Discrete Action Settings

https://github.com/paperwithcode/pop3d

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Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization

https://github.com/pierthodo/temporal_regularization

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Temporal Regularization in Markov Decision Process

https://github.com/pmalekzadeh/A-robust-quantile-huber-loss

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A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning

https://github.com/prabhath-r/FDQN_RL

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FDQN: A Flexible Deep Q-Network Framework for Game Automation

https://github.com/princeton-nlp/il-scaling-in-games

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Scaling Laws for Imitation Learning in Single-Agent Games

https://github.com/qgallouedec/lge

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Cell-Free Latent Go-Explore

https://github.com/qq456cvb/doudizhu-C

공식 코드

Combinational Q-Learning for Dou Di Zhu

https://github.com/quantumiracle/mars

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A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/quantumiracle/nash-dqn

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A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/rezabonyadi/atari_rl_arxiv

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Self Punishment and Reward Backfill for Deep Q-Learning

https://github.com/rl-interpretation/understandingRL

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Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution

https://github.com/rl-submissions/macro-credit-masp

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Meta-learning how to Share Credit among Macro-Actions

https://github.com/rlglab/minizero

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MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games

https://github.com/rlglab/optionzero

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OptionZero: Planning with Learned Options

https://github.com/rll-research/url_benchmark

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Behavior From the Void: Unsupervised Active Pre-Training

https://github.com/rllab/rllab

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Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/robinzixuan/IQN_Agent

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IGN : Implicit Generative Networks

https://github.com/rubenrtorrado/GVGAI_GYM

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Deep Reinforcement Learning for General Video Game AI

https://github.com/sacktock/ambs

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Approximate Model-Based Shielding for Safe Reinforcement Learning

https://github.com/sahiljain11/audio_rl

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Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implications

https://github.com/samlobel/cfn

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Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning

https://github.com/samuelschmidgall/evolutionaryselfreplication

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Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence

https://github.com/schmidtdominik/rainbow

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Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari

https://github.com/shagunsodhani/consistent-dynamics

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Learning Powerful Policies by Using Consistent Dynamics Model

https://github.com/sharathraparthy/polynomial-mixing-times

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Continual Learning In Environments With Polynomial Mixing Times

https://github.com/shashacks/symmetric_rl

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Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales

https://github.com/shayegano/CASL

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Crossmodal Attentive Skill Learner

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