SOTAVerified

Reinforcement Learning (RL)

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.

Papers

Showing 30763100 of 15113 papers

TitleStatusHype
Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot CollaborationCode0
A Study on Overfitting in Deep Reinforcement LearningCode0
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learningCode0
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI FeedbackCode0
Crafting a Toolchain for Image Restoration by Deep Reinforcement LearningCode0
Crafting desirable climate trajectories with RL explored socio-environmental simulationsCode0
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster LearningCode0
Weak Human Preference Supervision For Deep Reinforcement LearningCode0
CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement LearningCode0
Hybrid Actor-Critic Reinforcement Learning in Parameterized Action SpaceCode0
Learning to Drive in a DayCode0
Hybrid Reinforcement Learning with Expert State SequencesCode0
Hyp-RL : Hyperparameter Optimization by Reinforcement LearningCode0
Human level control through deep reinforcement learningCode0
Learning to Generalize for Sequential Decision MakingCode0
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning ApproachCode0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following AgentsCode0
HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal MemoryCode0
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile ManipulatorsCode0
Reward Shaping for Human Learning via Inverse Reinforcement LearningCode0
Human-Level Control without Server-Grade HardwareCode0
How to Build User Simulators to Train RL-based Dialog SystemsCode0
How to Make Deep RL Work in PracticeCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76Unverified
2PPOMean Normalized Performance0.58Unverified