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 4531–4540 of 15113 papers

TitleStatusHype
Epistemic Monte Carlo Tree Search—0
On the connection between Bregman divergence and value in regularized Markov decision processes—0
Implicit Offline Reinforcement Learning via Supervised Learning—0
Continual Vision-based Reinforcement Learning with Group Symmetries—0
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All NetworksCode0
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables—0
Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks—0
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents—0
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Benchmark Results

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