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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5851–5875 of 15113 papers

TitleStatusHype
Learning by shaking: Computing policy gradients by physical forward-propagation—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
Learning Combinatorial Node Labeling Algorithms—0
Learning Compact Reward for Image Captioning—0
Learning Complex Spatial Behaviours in ABM: An Experimental Observational Study—0
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots—0
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning—0
Learning Control for Air Hockey Striking using Deep Reinforcement Learning—0
Learning Controllable Elements Oriented Representations for Reinforcement Learning—0
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning—0
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner—0
Learning Curricula in Open-Ended Worlds—0
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior—0
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search—0
Learning Deterministic Policy with Target for Power Control in Wireless Networks—0
Learning Dexterous In-Hand Manipulation—0
Learning Dexterous Object Handover—0
Learning Dialog Policies from Weak Demonstrations—0
Learning Diverse Policies with Soft Self-Generated Guidance—0
Learning Dual-Arm Coordination for Grasping Large Flat Objects—0
Learning Dual-arm Object Rearrangement for Cartesian Robots—0
Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning—0
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach—0
Learning Dynamics and Generalization in Reinforcement Learning—0
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future—0
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Benchmark Results

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