https://github.com/thuml/SPOT
공식 코드Supported Policy Optimization for Offline Reinforcement Learning
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/thuml/TrajWorld
공식 코드https://github.com/thunil/Physics-Based-Deep-Learning
공식 코드https://github.com/thunlp-mt/museg
공식 코드MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
https://github.com/thuzouwenjun/mupo
공식 코드https://github.com/thward/neural_agent
공식 코드A Threshold-based Scheme for Reinforcement Learning in Neural Networks
https://github.com/thxsxth/POMDP_RLSepsis
공식 코드https://github.com/tiancigao/diffppo
공식 코드https://github.com/tianheyu927/PCGrad
공식 코드https://github.com/tianheyu927/mopo
공식 코드https://github.com/tianjunz/MADE
공식 코드MADE: Exploration via Maximizing Deviation from Explored Regions
https://github.com/tianjunz/hir
공식 코드The Wisdom of Hindsight Makes Language Models Better Instruction Followers
https://github.com/tianjunz/tempera
공식 코드https://github.com/tiantan522/PINs
공식 코드Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning
https://github.com/tianyucodings/diffusion_trusted_q_learning
공식 코드Diffusion Policies creating a Trust Region for Offline Reinforcement Learning
https://github.com/tidiane-camaret/contextual_rl_zero_shot
공식 코드Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning
https://github.com/timo-verstraeten/fwpi-experiments
공식 코드Fleet Control using Coregionalized Gaussian Process Policy Iteration
https://github.com/timotheemathieu/adaptive_stopping_mc_rl
공식 코드AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
https://github.com/tinker-twins/autorace-simulator
공식 코드Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
https://github.com/tinkoff-ai/cnf
공식 코드Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
https://github.com/tinnerhrhe/HIB_Policy
공식 코드Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective
https://github.com/tinnerhrhe/MTDiff
공식 코드Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
https://github.com/tirthankar95/numberlearninginchildren_rl_nlp
공식 코드https://github.com/tliu1997/arnpg-morl
공식 코드Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
https://github.com/tliu1997/rnac
공식 코드Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
https://github.com/tmdt-buw/karolos
공식 코드Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments
https://github.com/tmdt-buw/schlably
공식 코드schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
https://github.com/tmoer/multimodal_varinf
공식 코드Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
https://github.com/tmoer/return_distribution_exploration
공식 코드The Potential of the Return Distribution for Exploration in RL
https://github.com/tmvllrrl/its-study
공식 코드Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning
https://github.com/tnresearch/forlorn
공식 코드https://github.com/tobiabir/hambo
공식 코드Hallucinated Adversarial Control for Conservative Offline Policy Evaluation
https://github.com/tobiaslv/rad
공식 코드Conformal Symplectic Optimization for Stable Reinforcement Learning
https://github.com/tobsutter/pmdi_dro
공식 코드Robust Generalization despite Distribution Shift via Minimum Discriminating Information
https://github.com/tom1042roboai/aics_2022_causalcf
공식 코드Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
https://github.com/tom1042roboai/causalcf
공식 코드Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.