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

Showing 12011210 of 15113 papers

TitleStatusHype
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
Game-Theoretic Multiagent Reinforcement LearningCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest OverfittingCode1
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy GamesCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
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Benchmark Results

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