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

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Showing 4571–4580 of 15113 papers

TitleStatusHype
Rethinking Value Function Learning for Generalization in Reinforcement LearningCode1
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity—0
Model Predictive Control via On-Policy Imitation Learning—0
On Uncertainty in Deep State Space Models for Model-Based Reinforcement LearningCode1
Boosting Offline Reinforcement Learning via Data Rebalancing—0
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog SystemsCode0
Teacher Forcing Recovers Reward Functions for Text GenerationCode1
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning—0
You Only Live Once: Single-Life Reinforcement Learning—0
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Benchmark Results

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