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 1641–1650 of 15113 papers

TitleStatusHype
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from PixelsCode1
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement LearningCode1
Reinforcement Learning with Latent FlowCode1
MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal ControlCode1
Multi-Agent Trust Region LearningCode1
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient OptimizationCode1
Model-Based Visual Planning with Self-Supervised Functional DistancesCode1
Reinforcement Learning for Control of ValvesCode1
Augmenting Policy Learning with Routines Discovered from a Single DemonstrationCode1
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Benchmark Results

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