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Reinforcement Learning (RL)

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

공식 코드

Visual Grounding for Object-Level Generalization in Reinforcement Learning

https://github.com/pladosz/MOHQA

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Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture

https://github.com/plankson/a2po

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A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective

https://github.com/plankson/awesome-explainable-reinforcement-learning

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A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

https://github.com/plibin-vub/epi-rl

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Deep reinforcement learning for large-scale epidemic control

https://github.com/pmalekzadeh/A-robust-quantile-huber-loss

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A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning

https://github.com/pmalekzadeh/MM-KTD

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MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning

https://github.com/pmdidro/PMDI_DRO

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Robust Generalization despite Distribution Shift via Minimum Discriminating Information

https://github.com/pneuc/oparl

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Online Game Level Generation from Music

https://github.com/pokaxpoka/netrand

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Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

https://github.com/pokaxpoka/sunrise

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SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

https://github.com/polixir/NeoRL

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NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

https://github.com/polixir/neorl2

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NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios

https://github.com/porthok/RL_Simple_Shapes

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Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace

https://github.com/posgnu/rci-agent

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Language Models can Solve Computer Tasks

https://github.com/postech-cvlab/style-agnostic-rl

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Style-Agnostic Reinforcement Learning

https://github.com/poudel-bibek/Urban-Control

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Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning

https://github.com/pouyaAB/Accept_Synthetic_Objects_as_Real

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Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter

https://github.com/pouyahmdn/lcpo

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Online Reinforcement Learning in Non-Stationary Context-Driven Environments

https://github.com/ppocma/ppocma

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PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation

https://github.com/prabhatnagarajan/repro_dqn

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Deterministic Implementations for Reproducibility in Deep Reinforcement Learning

https://github.com/prajjwal1/rl_paradigm

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When should we prefer Decision Transformers for Offline Reinforcement Learning?

https://github.com/prashanm/StarCraft-II-causal-explanations

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Explainable Reinforcement Learning Through a Causal Lens

https://github.com/prasoongoyal/PixL2R

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PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards

https://github.com/pratikkunapuli/eqtrackingcontrol

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Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems

https://github.com/praveen-palanisamy/macad-gym

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Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning

https://github.com/praveenVnktsh/Modelling-Control-and-Simulation-of-a-Planar-Quadcopter

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Analysis and Control of a Planar Quadrotor

https://github.com/prime-rl/prime

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Process Reinforcement through Implicit Rewards

https://github.com/prime-rl/ttrl

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TTRL: Test-Time Reinforcement Learning

https://github.com/princeton-nlp/intercode

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InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback

https://github.com/princeton-rl/normalising-flows-4-reinforcement-learning

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Normalizing Flows are Capable Models for RL

https://github.com/prishita123/curriculum_rl_for_driving

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Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning

https://github.com/project-basileus/multitype-sequence-generation-by-tlstm-gan

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Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem

https://github.com/proppo/pipps_demo

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PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

https://github.com/proroklab/adversarial_comms

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The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning

https://github.com/proroklab/hetgppo

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Heterogeneous Multi-Robot Reinforcement Learning

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