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 1801–1810 of 15113 papers

TitleStatusHype
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing—0
Structural Design Through Reinforcement LearningCode0
Continuous Control with Coarse-to-fine Reinforcement Learning—0
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning—0
Preference-Guided Reinforcement Learning for Efficient ExplorationCode0
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning—0
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks—0
Periodic agent-state based Q-learning for POMDPs—0
On Bellman equations for continuous-time policy evaluation I: discretization and approximation—0
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Benchmark Results

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