https://github.com/saferl-lab/safe-marl-in-autonomous-driving
공식 코드Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous Driving
Papers with Code 아카이브
Q-Learning
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The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.
<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/saferoboticslab/safety_rl
공식 코드Safety and Liveness Guarantees through Reach-Avoid Reinforcement Learning
https://github.com/sail-sg/misa
공식 코드Mutual Information Regularized Offline Reinforcement Learning
https://github.com/saleh-faghfoorian/rl-rrt-kdtree
공식 코드Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone Connectivity
https://github.com/samuelfneumann/greedyac
공식 코드Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement
https://github.com/satchitchatterji/shieldedmarlthesis
공식 코드Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning
https://github.com/schroederdewitt/multiagent_mujoco
공식 코드https://github.com/scope-lab-vu/PAMCTS
공식 코드Decision Making in Non-Stationary Environments with Policy-Augmented Search
https://github.com/seanrsinclair/AdaptiveQLearning
공식 코드Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces
https://github.com/seohongpark/fql
공식 코드https://github.com/sergiorozada12/low-rank-rl
공식 코드https://github.com/sfujim/TD3
공식 코드Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/shahab-k93/cancer-and-smart-nanorobot
공식 코드https://github.com/shaneshixiang/rllabplusplus
공식 코드Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
https://github.com/shashacks/irl_transition
공식 코드Training Transition Policies via Distribution Matching for Complex Tasks
https://github.com/shreyassr123/double-sor-q-learning
공식 코드Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning
https://github.com/shreyassr123/multi-step-markov-games
공식 코드A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov Games
https://github.com/shukla-yash/lsts-icaps-24
공식 코드Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
https://github.com/shulong98/gail-pt
공식 코드https://github.com/sihongho/robust_marl_with_state_uncertainty
공식 코드Robust Multi-Agent Reinforcement Learning with State Uncertainty
https://github.com/simula-complex/deepqtest
공식 코드https://github.com/sliencerx/belief-enriched-robust-q-learning
공식 코드Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations
https://github.com/social-ai-uoft/ad-paper
공식 코드Temporal-Difference Learning Using Distributed Error Signals
https://github.com/spcornelius/blue_zero
공식 코드https://github.com/sriram94/matlql
공식 코드Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
https://github.com/sriram94/multiagentadvisorqlearning
공식 코드https://github.com/ssnl/poisson_quasimetric_embedding
공식 코드https://github.com/stepjam/ARM
공식 코드Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
https://github.com/stevezhangza/adverserial_density_regression
공식 코드Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression
https://github.com/stilwell-git/doubly-bounded-q-learning
공식 코드https://github.com/strath-ace/smart-ml
공식 코드A Novel Update Mechanism for Q-Networks Based On Extreme Learning Machines
https://github.com/sukiboo/policy_entropy
공식 코드Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
https://github.com/sweetice/POPO
공식 코드https://github.com/sweety-dm/interference-aware-deep-q-learning
공식 코드https://github.com/syyunn/finrl-dt
공식 코드Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading
https://github.com/taivu1998/FlapAI-Bird
공식 코드FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.