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 4071–4080 of 15113 papers

TitleStatusHype
Backward Curriculum Reinforcement Learning—0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management—0
A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management—0
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces—0
On Transforming Reinforcement Learning by Transformer: The Development Trajectory—0
Offline Policy Optimization in RL with Variance Regularizaton—0
Towards automating Codenames spymasters with deep reinforcement learning—0
On the Convergence of Discounted Policy Gradient Methods—0
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks—0
Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm—0
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Benchmark Results

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