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

Showing 14211430 of 15113 papers

TitleStatusHype
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement LearningCode1
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
ARLO: A Framework for Automated Reinforcement LearningCode1
Learning Rewards from Linguistic FeedbackCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
Fast Context Adaptation via Meta-LearningCode1
Show:102550
← PrevPage 143 of 1512Next →

Benchmark Results

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