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 3731–3740 of 15113 papers

TitleStatusHype
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning—0
Preference-Guided Reinforcement Learning for Efficient ExplorationCode0
On Bellman equations for continuous-time policy evaluation I: discretization and approximation—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks—0
Periodic agent-state based Q-learning for POMDPs—0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
FOSP: Fine-tuning Offline Safe Policy through World Models—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks—0
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Benchmark Results

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