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 2321–2330 of 15113 papers

TitleStatusHype
Bayesian policy selection using active inference—0
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation—0
Conditions on Features for Temporal Difference-Like Methods to Converge—0
Confidence-Conditioned Value Functions for Offline Reinforcement Learning—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient—0
Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence—0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents—0
Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach—0
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Benchmark Results

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