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 1221–1230 of 15113 papers

TitleStatusHype
A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication—0
Approximately Optimal Search on a Higher-dimensional Sliding PuzzleCode0
Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic PerspectiveCode2
Explore Reinforced: Equilibrium Approximation with Reinforcement Learning—0
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks—0
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations—0
Provable Partially Observable Reinforcement Learning with Privileged Information—0
Bilinear Convolution Decomposition for Causal RL Interpretability—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
o1-Coder: an o1 Replication for CodingCode3
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Benchmark Results

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