3 citations · 7 across the 6 of their papers we have counts for
6 papers
COSBO: Conservative Offline Simulation-Based Policy Optimization
Eshagh Kargar, Ville Kyrki
Offline reinforcement learning allows training reinforcement learning models on data from live deployments. However, it is limited to choosing the best combination of behaviors pre…
Learning Based High-Level Decision Making for Abortable Overtaking in Autonomous Vehicles
Ehsan Malayjerdi, Gokhan Alcan, Eshagh Kargar +3
Autonomous vehicles are a growing technology that aims to enhance safety, accessibility, efficiency, and convenience through autonomous maneuvers ranging from lane change to overta…
Vision Transformer for Learning Driving Policies in Complex Multi-Agent Environments
Eshagh Kargar, Ville Kyrki
Driving in a complex urban environment is a difficult task that requires a complex decision policy. In order to make informed decisions, one needs to gain an understanding of the l…
MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization
Eshagh Kargar, Ville Kyrki
This work considers the problem of learning cooperative policies in multi-agent settings with partially observable and non-stationary environments without a communication channel.…
Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning
Eshagh Kargar, Ville Kyrki
Driving in a dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision-making policy. The learning of such a policy requires a state repr…
Efficient Latent Representations using Multiple Tasks for Autonomous Driving
Eshagh Kargar, Ville Kyrki
Driving in the dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision policy. The learning of such a policy requires a state represent…