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Montezuma's Revenge

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 1
보존된 평가 기록: 3

Montezuma's Revenge is an ATARI 2600 Benchmark game that is known to be difficult to perform on for reinforcement learning algorithms. Solutions typically employ algorithms that incentivise environment exploration in different ways.

For the state-of-the art tables, please consult the parent Atari Games task.

<span style="color:grey; opacity: 0.6">( Image credit: Q-map )</span>

코드

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

https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/rnd_reward_model.py

Exploration by Random Network Distillation

https://github.com/prasoongoyal/rl-learn

Using Natural Language for Reward Shaping in Reinforcement Learning

https://github.com/riveSunder/carle

Exploration by Random Network Distillation

https://github.com/riveSunder/moving_in_morley

Exploration by Random Network Distillation

https://github.com/rle-foundation/rlexplore

Exploration by Random Network Distillation

https://github.com/robintyh1/icml2021-pengqlambda

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/roboticist-by-day/Rainbow_DQN

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/smj007/Breakout_A3C

Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay

https://github.com/thu-ml/tianshou

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/transedward/pytorch-hdqn

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/uoe-agents/derl

Exploration by Random Network Distillation

https://github.com/xusophia/DataSciFinalProj

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/yl3829/Q-map

Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

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