No current AI technique comes close to solving the TDW Transport Challenge.
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These actions create changes to the state of the agent and the environment.
The RL agent receives rewards based on how its actions bring it closer to its goal.
RL agents usually start by knowing nothing about their environment and selecting random actions.
As they gradually receive feedback from their environment, they learn sequences of actions that can maximize their rewards.
Reinforcement learning has also helped researchersmaster complicated gamessuch as Go, StarCraft 2, and DOTA.
Creating reinforcement learning models presents several challenges.
Another challenge is gathering training data.
Reinforcement learning agents need to train over data from millions of episodes of interactions with their environments.
To overcome this barrier, AI researchers have tried to create simulated environments for reinforcement learning applications.
Today, self-driving cars and robotics often use simulated environments as a major part of their training regime.
Task and motion planning
Reinforcement learning tests have different degrees of difficulty.
The challenge takes place in a multi-roomed house adorned with furniture, objects, and containers.
The agent is a two-armed robot, so it can only carry two objects at a time.
The agent receives a reward if it accomplishes the transfer task within a limited number of steps.
Through the TDW-Transport Challenge, were proposing a new embodied AI challenge, Gan said.
Pure deep reinforcement learning is not enough
The TDW-Transport Challenge is in the process of accepting submissions.
Their findings show that pure reinforcement learning is very poor at solving task and motion planning challenges.
Mobile robots are becoming ahot area of research and applications.
It will be interesting to see whether the TDW Transport Challenge will help usher new innovations in the field.
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