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Reinforcement Learning (RL)

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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/avivne/bilinear-transduction

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

https://github.com/awesomericky/complex-env-navigation

Learning agile and dynamic motor skills for legged robots

https://github.com/awesomericky/sim2real-grounding-simulator

Constrained Policy Optimization

https://github.com/awjuliani/Meta-RL

Learning to reinforcement learn

https://github.com/axelabels/Minecart

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

https://github.com/axkoenig/reflex_stack

The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms

https://github.com/axnedergaard/normalized-advantage-functions

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/aysaha/gym

https://github.com/ayucd/why-would-you-do-that

Deep Reinforcement Learning

https://github.com/ayusefi/Localization-Papers

One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay

https://github.com/azfeng8/pddlgym

PDDLGym: Gym Environments from PDDL Problems

https://github.com/b06b01073/Twin-Delayed-DDPG

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/b06b01073/continuous-control

Continuous control with deep reinforcement learning

https://github.com/bacdavid/HER

Continuous control with deep reinforcement learning

https://github.com/bacdavid/RND-O

Exploration by Random Network Distillation

https://github.com/backgom2357/Recommender_system_via_deep_RL

Continuous control with deep reinforcement learning

https://github.com/badrmarani/tsp-attn

Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem

https://github.com/bahh723/model-free-rl-algos

Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes

https://github.com/bainro/mcai

DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs

https://github.com/balloch/rl-exploration-transfer

Exploration by Random Network Distillation

https://github.com/baoguangsheng/g-transformer

Contextualized Rewriting for Text Summarization

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

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

https://github.com/barisozmen/deepaugment

Neural Architecture Search with Reinforcement Learning

https://github.com/bark-simulator/bark-ml

Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments

https://github.com/barmayo/spatial_attention

Visual Navigation with Spatial Attention

https://github.com/bastienvanderplaetse/action-branching-dqn

Action Branching Architectures for Deep Reinforcement Learning

https://github.com/batra-mlp-lab/visdial-rl

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

https://github.com/baturaysaglam/RIS-MISO-Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/baturaysaglam/TCD3

Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods

https://github.com/baturaysaglam/ris-miso-pda-deep-reinforcement-learning

Deep Intrinsically Motivated Exploration in Continuous Control

https://github.com/bay3s/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/bay3s/rl-squared

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/bayesian-optimization/bayesianoptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/bbartoldson/TBA

Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training

https://github.com/bchidamb/RL-Image-Classification

Natural Environment Benchmarks for Reinforcement Learning

https://github.com/bclyang/updated-atari-env

Surprising Negative Results for Generative Adversarial Tree Search

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