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 10501–10525 of 15113 papers

TitleStatusHype
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
Entropy Regularization for Mean Field Games with Learning—0
Entropy Regularized Reinforcement Learning with Cascading Networks—0
EnTRPO: Trust Region Policy Optimization Method with Entropy Regularization—0
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents—0
Environment Descriptions for Usability and Generalisation in Reinforcement Learning—0
Environment Generation for Zero-Shot Compositional Reinforcement Learning—0
Environment-Independent Task Specifications via GLTL—0
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation—0
Environment Shaping in Reinforcement Learning using State Abstraction—0
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines—0
Epersist: A Self Balancing Robot Using PID Controller And Deep Reinforcement Learning—0
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Episodic Memory Deep Q-Networks—0
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task—0
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited—0
Episodic Reinforcement Learning with Associative Memory—0
Epistemic Risk-Sensitive Reinforcement Learning—0
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning—0
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning—0
Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning—0
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Benchmark Results

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