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Proceedings of the 6th World Congress on Control and Automation, June 21 23, 2006, Dalian, China Rebased Optimization of Robotic Fish Behaviors Finding Liu, Hushing HU, Donging GU Department of Computer
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How to fill out rl-based optimisation of robotic:

01
Understand the concept of reinforcement learning (RL) and its application in robotics. RL-based optimisation involves training robots to learn from their environment and make decisions based on feedback and rewards.
02
Familiarize yourself with different RL algorithms and techniques that can be used for robotic optimisation. This includes methods such as Q-learning, policy gradients, and deep Q-networks.
03
Define the specific goals and objectives of the robotic optimisation project. Determine what aspects of the robot's behavior or performance you want to improve using RL.
04
Gather and preprocess relevant data for training the RL model. This may involve collecting sensor data, designing simulated environments, or using existing datasets.
05
Design the RL agent and choose appropriate features, state representations, and action spaces for your robotic optimisation problem. Consider factors such as the robot's environment, capabilities, and constraints.
06
Implement the RL algorithm and train the robotic agent using the collected data. This involves iterating through episodes and updating the agent's policies and value functions based on the observed rewards and actions.
07
Evaluate the trained RL agent's performance using various metrics and benchmarks. Measure how well the robot's optimisation response matches the desired goals and objectives set in step 3.
08
Fine-tune and iterate the RL-based optimisation process to improve the robot's performance. Adjust parameters, explore different RL algorithms, or collect additional data if needed.

Who needs rl-based optimisation of robotic?

01
Researchers and engineers working in the field of robotics who want to enhance the capabilities and performance of their robotic systems.
02
Industries that rely on robotic automation for tasks with complex environments, uncertain dynamics, or changing conditions. RL-based optimisation can help improve the adaptability and intelligence of robots in such settings.
03
Robotics enthusiasts and hobbyists who want to explore the potential of RL in training autonomous robots and creating innovative robotic applications.

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