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Found 13 papers, 6 papers with code
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories
By retrospectively deconstructing the search tree into multiple paths each contained within a sub-tree, we enable the agent to learn from shorter trajectories with more predictable next states.
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration
Compared to the nearest competitor, ECORD reduces the optimality gap by up to 73% on 500 vertex graphs with a decreased wall-clock time.
One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning
Self-tuning algorithms that adapt the learning process online encourage more effective and robust learning.
Learning to Solve Combinatorial Problems via Efficient Exploration
Compared to the nearest competitor, ECORD reduces the optimality gap by up to 73% on 500 vertex graphs with a decreased wall-clock time.
Mava: a research framework for distributed multi-agent reinforcement learning
We provide experimental results for these implementations on a wide range of multi-agent environments and highlight the benefits of distributed system training.
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning
Furthermore, the core utility of our imagination is deeply coupled with communication.
Factored Action Spaces in Deep Reinforcement Learning
Very large action spaces constitute a critical challenge for deep Reinforcement Learning (RL) algorithms.
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning
This suggests that combining leading protein design methods with modern deep reinforcement learning is a viable path for discovering a Covid-19 cure and may accelerate design of peptide-based therapeutics for other diseases.
Offline Reinforcement Learning Hands-On
Offline Reinforcement Learning (RL) aims to turn large datasets into powerful decision-making engines without any online interactions with the environment.
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control.
Learning Compositional Neural Programs for Continuous Control
Third, the self-models are harnessed to learn recursive compositional programs with multiple levels of abstraction.
Learning Compositional Neural Programs with Recursive Tree Search and Planning
AlphaZero contributes powerful neural network guided search algorithms, which we augment with recursion.
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
Results from applying the R2 algorithm to instances of a two-dimensional and three-dimensional bin packing problems show that it outperforms generic Monte Carlo tree search, heuristic algorithms and integer programming solvers.
