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 4131–4140 of 15113 papers

TitleStatusHype
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes—0
Fast Value Tracking for Deep Reinforcement Learning—0
Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT EnvironmentsCode0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers—0
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents—0
Decomposing Control Lyapunov Functions for Efficient Reinforcement LearningCode0
The Value of Reward Lookahead in Reinforcement Learning—0
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards—0
Offline Multitask Representation Learning for Reinforcement Learning—0
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Benchmark Results

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