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 1481–1490 of 15113 papers

TitleStatusHype
Same State, Different Task: Continual Reinforcement Learning without InterferenceCode1
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement LearningCode1
Online reinforcement learning with sparse rewards through an active inference capsuleCode1
Differentiable Architecture Search for Reinforcement LearningCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Offline Reinforcement Learning as One Big Sequence Modeling ProblemCode1
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement LearningCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Q-attention: Enabling Efficient Learning for Vision-based Robotic ManipulationCode1
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
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Benchmark Results

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