SOTAVerified

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

Showing 411420 of 514 papers

TitleStatusHype
Randomized Value Functions via Multiplicative Normalizing FlowsCode0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Exploratory State Representation LearningCode0
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human PriorsCode0
Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal GuidanceCode0
Go Beyond Imagination: Maximizing Episodic Reachability with World ModelsCode0
Receding Horizon CuriosityCode0
Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability ObjectivesCode0
Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability GraphsCode0
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse RewardsCode0
Show:102550
← PrevPage 42 of 52Next →

No leaderboard results yet.