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 3421–3430 of 15113 papers

TitleStatusHype
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle—0
Countering Language Drift via Grounding—0
A Study of Continual Learning Methods for Q-Learning—0
A unified strategy for implementing curiosity and empowerment driven reinforcement learning—0
A Study of AI Population Dynamics with Million-agent Reinforcement Learning—0
Counterfactual Regularization for Model-Based Reinforcement Learning—0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
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Benchmark Results

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