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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/facebookresearch/ppo-dice

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Stable Policy Optimization via Off-Policy Divergence Regularization

https://github.com/facebookresearch/rebel

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Combining Deep Reinforcement Learning and Search for Imperfect-Information Games

https://github.com/facebookresearch/reward-estimator-corl

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Reward Estimation for Variance Reduction in Deep Reinforcement Learning

https://github.com/facebookresearch/salina

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Learning a subspace of policies for online adaptation in Reinforcement Learning

https://github.com/facebookresearch/sound-spaces

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SoundSpaces: Audio-Visual Navigation in 3D Environments

https://github.com/ferretdoll/hgr

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Hologram Reasoning for Solving Algebra Problems with Geometry Diagrams

https://github.com/fietelab/farcuriosity

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Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity

https://github.com/financialcomputingucl/drl_for_active_high_frequency_trading

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Deep Reinforcement Learning for Active High Frequency Trading

https://github.com/flexi-framework/drl_les

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Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations

https://github.com/flexi-framework/relexi

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Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems

https://github.com/florensacc/snn4hrl

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Stochastic Neural Networks for Hierarchical Reinforcement Learning

https://github.com/florentdelgrange/wae_mdp

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Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees

https://github.com/floringogianu/snrl

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Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective

https://github.com/flow-project/flow

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Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/flowersteam/EpidemiOptim

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EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

https://github.com/flowersteam/Imagine

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Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration

https://github.com/flowersteam/TeachMyAgent

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TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL

https://github.com/flowersteam/geppg

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GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

https://github.com/flowersteam/playground_env

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Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration

https://github.com/flowersteam/rl-difference-testing

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How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments

https://github.com/flowersteam/teachDeepRL

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Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

https://github.com/flowss/rlmutation

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Mutation Testing of Deep Reinforcement Learning Based on Real Faults

https://github.com/flyao123/district-microgrid-dataset

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A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning

https://github.com/fmorenovr/DRDQ-Atari

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Performing Deep Recurrent Double Q-Learning for Atari Games

https://github.com/fmpr/CAREL

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Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

https://github.com/fortsandmills/rl-theory-book

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Reinforcement Learning Textbook

https://github.com/fouzibenfaid/prosky

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ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G

https://github.com/fracapuano/temporl

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TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning

https://github.com/francelico/deac-rep

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Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning

https://github.com/francescodelellis/reward-shaping-for-rl-based-control

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Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning

https://github.com/fredericometelo/peersim-environment

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PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning

https://github.com/fredericometelo/taskoffloadingagentlibrary

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PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning

https://github.com/frenkowski/scimai-gym

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Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains

https://github.com/frt03/inference-based-rl

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Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning

https://github.com/frt03/pic

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Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

https://github.com/fslight1996/SHER

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Soft Hindsight Experience Replay

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