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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://github.com/omardrwch/kernel_ucbvi_experiments

공식 코드

Kernel-Based Reinforcement Learning: A Finite-Time Analysis

https://github.com/omarshindi/model-free-quantum-gate-design-and-calibration-using-deep-reinforcement-learning

공식 코드

Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning

https://github.com/omron-sinicx/action-constrained-rl-benchmark

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Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints

https://github.com/omron-sinicx/shinrl

공식 코드

ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

https://github.com/omron-sinicx/sliceit

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SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing

https://github.com/on1262/deep-reasoning

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Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning

https://github.com/ondrejba/action_priors

공식 코드

Action Priors for Large Action Spaces in Robotics

https://github.com/onur4229/svsl_lmoe

공식 코드

Specializing Versatile Skill Libraries using Local Mixture of Experts

https://github.com/open-thought/reasoning-gym

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ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

https://github.com/openai/EPG

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Evolved Policy Gradients

https://github.com/openai/Video-Pre-Training

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Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

https://github.com/openai/baselines

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Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/openai/coinrun

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Quantifying Generalization in Reinforcement Learning

https://github.com/openai/evolution-strategies-starter

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Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/openai/lm-human-preferences

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Fine-Tuning Language Models from Human Preferences

https://github.com/openai/multi-agent-emergence-environments

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Emergent Tool Use From Multi-Agent Autocurricula

https://github.com/openai/multiagent-particle-envs

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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/openai/phasic-policy-gradient

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Phasic Policy Gradient

https://github.com/openai/prm800k

공식 코드

Let's Verify Step by Step

https://github.com/openai/procgen

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Leveraging Procedural Generation to Benchmark Reinforcement Learning

https://github.com/openai/random-network-distillation

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Exploration by Random Network Distillation

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

공식 코드

Leveraging Procedural Generation to Benchmark Reinforcement Learning

https://github.com/opendilab/GenerativeRL

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Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective

https://github.com/opendilab/LightZero

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UniZero: Generalized and Efficient Planning with Scalable Latent World Models

https://github.com/opengvlab/gitm

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Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory

https://github.com/openpsi-project/srl

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SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores

https://github.com/openpsi-projects/srl

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SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores

https://github.com/openrl-lab/openrl

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OpenRL: A Unified Reinforcement Learning Framework

https://github.com/orcax/PGPR

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Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

https://github.com/orenneumann/alphazero-scaling-laws

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Scaling Laws for a Multi-Agent Reinforcement Learning Model

https://github.com/ori-drs/lfmc_cval

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Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

https://github.com/ori-drs/lfmc_gym

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Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

https://github.com/ori-drs/lfmc_pyval

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Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion

https://github.com/oscarhuangwind/drl-transformer-simtoreal-navigation

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Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

https://github.com/osh/kerlym

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Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent

https://github.com/osikazuzanna/bi-objective-clustering

공식 코드

Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning

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