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Found 4 papers, 1 papers with code
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-ReIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes.
Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID-19 Pandemic
In turn, significant increases in traffic congestion are expected, since people are likely to prefer using their own vehicles or taxis as opposed to riskier and more crowded options such as the railway.
Emergent Prosociality in Multi-Agent Games Through Gifting
Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game.
Influencing Towards Stable Multi-Agent Interactions
Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which can restrain the non-stationarity caused by the other agent.
