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 3281–3290 of 15113 papers

TitleStatusHype
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
Dealing with Sparse Rewards Using Graph Neural Networks—0
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning—0
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation—0
INTAGS: Interactive Agent-Guided Simulation—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
Death and Suicide in Universal Artificial Intelligence—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
Show:102550
← PrevPage 329 of 1512Next →

Benchmark Results

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