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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 151–175 of 514 papers

TitleStatusHype
Evolutionary Reinforcement Learning via Cooperative Coevolution—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Autonomous synthesis of metastable materials—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
Efficient exploration of zero-sum stochastic games—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
Efficient exploration with Double Uncertain Value Networks—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Context-Dependent Upper-Confidence Bounds for Directed Exploration—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Exploration by Learning Diverse Skills through Successor State Measures—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
Efficient Policy Space Response Oracles—0
Efficient Pose and Cell Segmentation using Column Generation—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
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