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 421–430 of 15113 papers

TitleStatusHype
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
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Benchmark Results

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