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 4076–4100 of 15113 papers

TitleStatusHype
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance—0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning—0
Action Redundancy in Reinforcement Learning—0
Action Set Based Policy Optimization for Safe Power Grid Management—0
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos—0
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks—0
Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning—0
Active Alignments of Lens Systems with Reinforcement Learning—0
Active Classification of Moving Targets with Learned Control Policies—0
Active Coverage for PAC Reinforcement Learning—0
Active Deep Q-learning with Demonstration—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Active Hierarchical Imitation and Reinforcement Learning—0
Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning—0
Active Inference as a Model of Agency—0
Active Information Acquisition—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active Measure Reinforcement Learning for Observation Cost Minimization—0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning—0
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach—0
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning—0
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework—0
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems—0
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Benchmark Results

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