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 3001–3010 of 15113 papers

TitleStatusHype
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Asynchronous Coagent Networks—0
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup—0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—0
Adapting the Function Approximation Architecture in Online Reinforcement Learning—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
Deep Curiosity Loops in Social Environments—0
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Benchmark Results

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