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Found 7 papers, 2 papers with code
Correlation Priors for Reinforcement Learning
Many decision-making problems naturally exhibit pronounced structures inherited from the characteristics of the underlying environment.
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.
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention.
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.
A Bayesian Approach to Policy Recognition and State Representation Learning
Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert.
Inverse Reinforcement Learning in Swarm Systems
Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data.
