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 3231–3240 of 15113 papers

TitleStatusHype
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Data Driven Reward Initialization for Preference based Reinforcement Learning—0
Data-Driven Robust Control Using Reinforcement Learning—0
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning—0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
A Survey of Continual Reinforcement Learning—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
Credit Assignment Techniques in Stochastic Computation Graphs—0
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Benchmark Results

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