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Deep Reinforcement Learning

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코드

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

https://github.com/bluecontra/CHAIN

공식 코드

Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn

https://github.com/bnn-upc/drl-es-otn

공식 코드

Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

https://github.com/boettiger-lab/approx-model-or-approx-soln

공식 코드

Pretty darn good control: when are approximate solutions better than approximate models

https://github.com/boettiger-lab/rl-intro

공식 코드

Deep Reinforcement Learning for Conservation Decisions

https://github.com/boschresearch/DD_OPG

공식 코드

Trajectory-Based Off-Policy Deep Reinforcement Learning

https://github.com/boschresearch/trust-region-layers

공식 코드

Differentiable Trust Region Layers for Deep Reinforcement Learning

https://github.com/boschresearch/ube-mbrl

공식 코드

Model-Based Uncertainty in Value Functions

https://github.com/braemt/attentive-multi-task-deep-reinforcement-learning

공식 코드

Attentive Multi-Task Deep Reinforcement Learning

https://github.com/bramgrooten/automatic-noise-filtering

공식 코드

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning

https://github.com/bramgrooten/deeprl-for-hanabi

공식 코드

Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi

https://github.com/bramgrooten/mask-distractions

공식 코드

MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning

https://github.com/brett-daley/dqn-lambda

공식 코드

Reconciling $λ$-Returns with Experience Replay

https://github.com/brett-daley/fast-dqn

공식 코드

Human-Level Control without Server-Grade Hardware

https://github.com/brett-daley/virtual-replay-cache

공식 코드

Virtual Replay Cache

https://github.com/buaawgj/tace

공식 코드

Adaptive trajectory-constrained exploration strategy for deep reinforcement learning

https://github.com/burcukoglu/p4o-predictiveprocessingppo

공식 코드

Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization

https://github.com/cair/DeepRTS

공식 코드

Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

https://github.com/cair/deep-warehouse

공식 코드

Towards Model-based Reinforcement Learning for Industry-near Environments

https://github.com/camall3n/markov-state-abstractions

공식 코드

Learning Markov State Abstractions for Deep Reinforcement Learning

https://github.com/carloderamo/shared/tree/master

공식 코드

Sharing Knowledge in Multi-Task Deep Reinforcement Learning

https://github.com/cassidylaidlaw/effective-horizon

공식 코드

Bridging RL Theory and Practice with the Effective Horizon

https://github.com/cathywu/flow

공식 코드

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/cav-research-lab/predictive-model-delay-correction

공식 코드

Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays

https://github.com/cav-research-lab/safe-reinforcement-learning-using-symbolic-logical-programming-for-autonomous-highway-driving

공식 코드

Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach

https://github.com/cbellinger27/bendrl_reacher_tracker

공식 코드

Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm

https://github.com/ch9397/fixedwing-monoppo

공식 코드

Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs

https://github.com/chainer/chainerrl

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ChainerRL: A Deep Reinforcement Learning Library

https://github.com/chandar-lab/CMAL_Hanabi

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Continuous Coordination As a Realistic Scenario for Lifelong Learning

https://github.com/chandar-lab/Lifelong-Hanabi

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Continuous Coordination As a Realistic Scenario for Lifelong Learning

https://github.com/charlie0257/t2tl

공식 코드

Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion Planning

https://github.com/chengzelei/rice

공식 코드

RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation

https://github.com/chenhongge/StateAdvDRL

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Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

https://github.com/chenziku/train-procgen

공식 코드

Reinforcement Learning Generalization with Surprise Minimization

https://github.com/chkwon/TSPDrone-RL

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A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone

https://github.com/cho-geonwoo/traced

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TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design

https://github.com/christianll9/tsppc-drl

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Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning

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