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 4581–4590 of 15113 papers

TitleStatusHype
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments—0
An Actor-Critic Method for Simulation-Based Optimization—0
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation—0
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples—0
An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems—0
An adaptive synchronization approach for weights of deep reinforcement learning—0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario—0
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
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Benchmark Results

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