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 2291–2300 of 15113 papers

TitleStatusHype
Behavioral Differences is the Key of Ad-hoc Team Cooperation in Multiplayer Games Hanabi—0
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Adaptive trading strategies across liquidity pools—0
Be Considerate: Objectives, Side Effects, and Deciding How to Act—0
Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit—0
A Complementary Learning Systems Approach to Temporal Difference Learning—0
Analysis of Reinforcement Learning for determining task replication in workflows—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Computational Model of Music Sight Reading: A Reinforcement Learning Approach—0
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Benchmark Results

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