https://github.com/se-hwan/pbrs-humanoid
공식 코드Benchmarking Potential Based Rewards for Learning Humanoid Locomotion
Papers with Code 아카이브
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/seanjia/gsl
공식 코드Improving Policy Optimization with Generalist-Specialist Learning
https://github.com/seanjparker/rlflow
공식 코드RLFlow: Optimising Neural Network Subgraph Transformation with World Models
https://github.com/seanrsinclair/AdaptiveQLearning
공식 코드Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces
https://github.com/seanrsinclair/hindsight-learning
공식 코드https://github.com/seawee1/driver-dojo
공식 코드Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
https://github.com/seba-1511/shapechanger
공식 코드https://github.com/sebascuri/hucrl
공식 코드Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
https://github.com/sebastianrisi/ga-world-models
공식 코드https://github.com/sebjai/robust-risk-aware-rl
공식 코드https://github.com/semitrivial/extendedenvironments
공식 코드Extending Environments To Measure Self-Reflection In Reinforcement Learning
https://github.com/sensl/andes_gym
공식 코드Andes_gym: A Versatile Environment for Deep Reinforcement Learning in Power Systems
https://github.com/seohongpark/fql
공식 코드https://github.com/seohongpark/hilp
공식 코드https://github.com/seohongpark/hiql
공식 코드HIQL: Offline Goal-Conditioned RL with Latent States as Actions
https://github.com/seohongpark/metra
공식 코드METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
https://github.com/seohongpark/ogbench
공식 코드https://github.com/seohongpark/pma
공식 코드https://github.com/seongun-kim/vcrl
공식 코드Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
https://github.com/ser-ge/ask_before_you_act
공식 코드Ask Before You Act: Generalising to Novel Environments by Asking Questions
https://github.com/sergiorozada12/tensor-low-rank-pg
공식 코드Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning
https://github.com/sergiorozada12/tensor-low-rank-rl
공식 코드Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
https://github.com/setarehc/deep_rl_regions
공식 코드Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
https://github.com/seungyulhan/dac
공식 코드Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
https://github.com/seungyulhan/disc
공식 코드Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
https://github.com/seungyulhan/mme
공식 코드https://github.com/sff1019/opirl
공식 코드OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching
https://github.com/sfujim/BCQ
공식 코드https://github.com/sfujim/LAP-PAL
공식 코드An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
https://github.com/sfujim/SR-DICE
공식 코드https://github.com/sfujim/TD3
공식 코드Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/sfujim/TD3_BC
공식 코드https://github.com/sfujim/td7
공식 코드For SALE: State-Action Representation Learning for Deep Reinforcement Learning
https://github.com/sgdgp/virtualhome_irl
공식 코드Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning
https://github.com/sgillen/policy_refinement
공식 코드Direct Random Search for Fine Tuning of Deep Reinforcement Learning Policies
https://github.com/shacklettbp/bps-nav
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.