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 3571–3580 of 15113 papers

TitleStatusHype
Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics—0
Efficient Learning of High Level Plans from Play—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Goal-conditioned Offline Reinforcement Learning through State Space Partitioning—0
Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning—0
Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning—0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Recommending the optimal policy by learning to act from temporal data—0
Reinforcement Learning for Omega-Regular Specifications on Continuous-Time MDP—0
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning—0
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Benchmark Results

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