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 11051–11075 of 15113 papers

TitleStatusHype
Training in Task Space to Speed Up and Guide Reinforcement Learning—0
Training Language Models to Critique With Multi-agent Feedback—0
Training Large Language Models to Reason via EM Policy Gradient—0
Training Larger Networks for Deep Reinforcement Learning—0
Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field—0
LeDex: Training LLMs to Better Self-Debug and Explain Code—0
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators—0
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior—0
Trajectory-based Learning for Ball-in-Maze Games—0
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning—0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Trajectory First: A Curriculum for Discovering Diverse Policies—0
Trajectory Modeling via Random Utility Inverse Reinforcement Learning—0
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning—0
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces—0
Trajectory representation learning for Multi-Task NMRDPs planning—0
Trajectory Tracking of Underactuated Sea Vessels With Uncertain Dynamics: An Integral Reinforcement Learning Approach—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving—0
TransDreamer: Reinforcement Learning with Transformer World Models—0
Transferable Curricula through Difficulty Conditioned Generators—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots—0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents—0
Distributional Successor Features Enable Zero-Shot Policy Optimization—0
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Benchmark Results

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