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 1461–1470 of 15113 papers

TitleStatusHype
Reinforcement Learning in Hyperbolic Spaces: Models and Experiments—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree SearchCode0
Physical Simulation for Multi-agent Multi-machine Tending—0
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free ControlCode0
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Reinforcement Learning for Control of Non-Markovian Cellular Population DynamicsCode0
Words as Beacons: Guiding RL Agents with High-Level Language Prompts—0
Can we hop in general? A discussion of benchmark selection and design using the Hopper environment—0
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Benchmark Results

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