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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8526–8550 of 15113 papers

TitleStatusHype
On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models—0
On the Modeling Capabilities of Large Language Models for Sequential Decision Making—0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning—0
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation—0
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal—0
On the Power of Multitask Representation Learning in Linear MDP—0
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness—0
On the Practical Consistency of Meta-Reinforcement Learning Algorithms—0
On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning—0
On the Reduction of Variance and Overestimation of Deep Q-Learning—0
On the Relationship Between Active Inference and Control as Inference—0
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent—0
On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement—0
On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems—0
On the Role of Discount Factor in Offline Reinforcement Learning—0
On the role of planning in model-based deep reinforcement learning—0
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer—0
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation—0
The Curse of Passive Data Collection in Batch Reinforcement Learning—0
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization—0
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples—0
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning—0
On the Search for Feedback in Reinforcement Learning—0
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems—0
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning—0
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Benchmark Results

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