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 7576–7600 of 15113 papers

TitleStatusHype
Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assembly—0
Generalized Hindsight for Reinforcement Learning—0
Generalized Maximum Causal Entropy for Inverse Reinforcement Learning—0
Generalized Maximum Entropy Reinforcement Learning via Reward Shaping—0
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence—0
Generalized Off-Policy Actor-Critic—0
Generalized Planning With Deep Reinforcement Learning—0
Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts—0
Generalized Reinforcement Learning for Building Control using Behavioral Cloning—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—0
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization—0
Generalizing Curricula for Reinforcement Learning—0
Generalizing from a few environments in safety-critical reinforcement learning—0
Generalizing Reinforcement Learning to Unseen Actions—0
Generalizing Skills with Semi-Supervised Reinforcement Learning—0
Generalizing Successor Features to continuous domains for Multi-task Learning—0
General Method for Solving Four Types of SAT Problems—0
General sum stochastic games with networked information flows—0
Generate and Revise: Reinforcement Learning in Neural Poetry—0
Generating and Evolving Reward Functions for Highway Driving with Large Language Models—0
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model—0
Generating Critical Scenarios for Testing Automated Driving Systems—0
Generating Explanations from Deep Reinforcement Learning Using Episodic Memory—0
Generating Formality-Tuned Summaries Using Input-Dependent Rewards—0
Generating GPU Compiler Heuristics using Reinforcement Learning—0
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Benchmark Results

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