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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://bitbucket.org/RLinRL/intelligenttrainerpublic

공식 코드

Intelligent Trainer for Model-Based Reinforcement Learning

https://bitbucket.org/RToroIcarte/qrm

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Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning

https://bitbucket.org/ZimingLi/dg-irl-aaai2019

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Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

https://bitbucket.org/act-lab/release

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Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation

https://bitbucket.org/catk1ns0n/repr_public

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Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting

https://bitbucket.org/cltl/redep-java

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Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing

https://bitbucket.org/helges/retecs

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Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

https://bitbucket.org/scee_ietr/malin-multi-armed-bandit-learning-for-iot-networks-with-grc

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Reinforcement Learning for Physical Layer Communications

https://bitbucket.org/thiagomestrado/journalarticle

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Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial Puzzles

https://bitbucket.org/tsendeemts/mnm

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Metalearned Neural Memory

https://github.com/011235813/SEPT

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Single Episode Policy Transfer in Reinforcement Learning

https://github.com/011235813/cm3

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CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

https://github.com/011235813/hierarchical-marl

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Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery

https://github.com/011235813/lio

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Learning to Incentivize Other Learning Agents

https://github.com/011235813/marl-amr

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Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

https://github.com/1jsingh/semantic-guidance

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Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings

https://github.com/2019chengong/offline_rl_poisoner

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BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets

https://github.com/2019chengong/trajdeleter

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TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents

https://github.com/2664139264/sequential_rl

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Constructing Non-Markovian Decision Process via History Aggregator

https://github.com/289371298/ceip

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CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations

https://github.com/2Groza/PCHID_code

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Policy Continuation with Hindsight Inverse Dynamics

https://github.com/2groza/action-refined-temporal-difference

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Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference

https://github.com/32af3611/ai4mat-neurips-workshop-2022

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Actively Learning Costly Reward Functions for Reinforcement Learning

https://github.com/42jaylonw/rrc2021threewolves

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Real-World Dexterous Object Manipulation based Deep Reinforcement Learning

https://github.com/4thhia/differential_TD

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A Temporal Difference Method for Stochastic Continuous Dynamics

https://github.com/5vision/DARQN

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Deep Attention Recurrent Q-Network

https://github.com/7777777cc/code

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BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning

https://github.com/846468230/RoSGAS

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RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search

https://github.com/98k-bot/GAN-Assisted-Preference-Based-Learning

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Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach

https://github.com/99andBeyond/Apollo1060

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Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

https://github.com/99andBeyond/max-bellman-toy

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Maximum Reward Formulation In Reinforcement Learning

https://github.com/A2R-Lab/Just-Round

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Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion

https://github.com/AI4Finance-Foundation/FinRL

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FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

https://github.com/AIRLab-POLIMI/mushroom-rl

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MushroomRL: Simplifying Reinforcement Learning Research

https://github.com/ALRhub/fancy_gym

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Deep Black-Box Reinforcement Learning with Movement Primitives

https://github.com/Aakriti05/Prop-Fail-Detect-Control-RL

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Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning

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