Efficient Exploration
144 papers with code • 0 benchmarks • 2 datasets
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
Benchmarks
These leaderboards are used to track progress in Efficient Exploration
Libraries
Use these libraries to find Efficient Exploration models and implementations
Replica
House3D Environment
Most implemented papers
Self-Supervised Exploration via Disagreement
pathak22/exploration-by-disagreement
•
•
In this paper, we propose a formulation for exploration inspired by the work in active learning literature.
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood
The vehicle routing problem is one of the most studied combinatorial optimization topics, due to its practical importance and methodological interest.
BeBold: Exploration Beyond the Boundary of Explored Regions
In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.
State Entropy Maximization with Random Encoders for Efficient Exploration
Recent exploration methods have proven to be a recipe for improving sample-efficiency in deep reinforcement learning (RL).
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
hyunghona/emu
•
•
NeurIPS 2021
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
Online Decision Transformer
facebookresearch/online-dt
•
•
Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches similar to large-scale language modeling.
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot
med-air/dex
•
•
Task automation of surgical robot has the potentials to improve surgical efficiency.
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
Generalization and Exploration via Randomized Value Functions
qdevpsi3/randomized-value-iteration
•
•
We propose randomized least-squares value iteration (RLSVI) -- a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized value functions.
Batch Bayesian Optimization via Local Penalization
The approach assumes that the function of interest, $f$, is a Lipschitz continuous function.
