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Found 5 papers, 3 papers with code
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization
Before each update, it solves the trust region problem for an optimal step size, resulting in a more stable and faster optimization process.
Regret-Aware Black-Box Optimization with Natural Gradients, Trust-Regions and Entropy Control
In contrast, stochastic optimizers that are motivated by policy gradients, such as the Model-based Relative Entropy Stochastic Search (MORE) algorithm, directly optimize the expected fitness function without the use of rankings.
Deep Reinforcement Learning for Swarm Systems
However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant.
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents.
Guided Deep Reinforcement Learning for Swarm Systems
Here, we follow a guided approach where a critic has central access to the global state during learning, which simplifies the policy evaluation problem from a reinforcement learning point of view.
