Papers with Code 아카이브

Atari Games

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
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/omkarv/pong-from-pixels

Playing Atari with Deep Reinforcement Learning

https://github.com/ondrejba/action_attention_iclr_22

Contrastive Learning of Structured World Models

https://github.com/openai/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/openai/universe-starter-agent

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/opendilab/DI-engine

Deep Reinforcement Learning with Double Q-learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/c51.py

A Distributional Perspective on Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/impala.py

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/opendilab/DI-engine/blob/main/ding/policy/iqn.py

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qrdqn.py

Distributional Reinforcement Learning with Quantile Regression

https://github.com/opendilab/DI-engine/blob/main/ding/policy/r2d2.py

https://github.com/opendilab/DI-engine/blob/main/ding/policy/rainbow.py

Rainbow: Combining Improvements in Deep Reinforcement Learning

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

Exploration by Random Network Distillation

https://github.com/opendilab/LightZero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/openrlbenchmark/openrlbenchmark

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

https://github.com/opplieam/Pong-Deep-RL

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/paintception/Deep-Quality-Value-DQV-Learning-

Deep Quality-Value (DQV) Learning

https://github.com/paintception/Deep-Quality-Value-Family

Playing Atari with Deep Reinforcement Learning

https://github.com/paintception/Deep-Quality-Value-Family-

Playing Atari with Deep Reinforcement Learning

https://github.com/parilo/gym_bipedal_walker_v2_solution

A Distributional Perspective on Reinforcement Learning

https://github.com/parilo/rl-server

Playing Atari with Deep Reinforcement Learning

https://github.com/pat-coady/trpo

Trust Region Policy Optimization

https://github.com/pathway/alphaxos

Deep Reinforcement Learning with Double Q-learning

https://github.com/patniemeyer/ga-autoencoder

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/pauldebryas/SND_trackers_My_data

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/pavitrakumar78/Playing-custom-games-using-Deep-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/pereirarodrigo/project_spike

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

https://github.com/pereirarodrigo/spike

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

https://github.com/philtabor/Deep-Q-Learning-Paper-To-Code

Deep Reinforcement Learning with Double Q-learning

https://github.com/pihey1995/DistributionalRL

A Distributional Perspective on Reinforcement Learning

https://github.com/pipiPdesu/optimize-coordconv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/pku-rl/copl

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

https://github.com/pocokhc/agent57

Agent57: Outperforming the Atari Human Benchmark

https://github.com/prajwalgatti/DRL-Continuous-Control

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/prajwalgatti/DRL-Navigation

Dueling Network Architectures for Deep Reinforcement Learning

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

Using Natural Language for Reward Shaping in Reinforcement Learning

https://github.com/proroklab/popgym

Playing Atari with Deep Reinforcement Learning

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