## 4 Mobile Robot Local Path Planning
This paper has addressed the overfitting problem of model-based robots. The research has simulated the robot in a 2D scenario and has shown a reduction in learning time compared to model-free RL algorithms. Imitation learning is another related approach that uses demonstrations by experts operating robots for specific tasks to obtain policy functions. Mobile robots learn mappings between observations of these demonstrations and appropriate robot actions. Path trajectories can be rapidly generated by manipulating learned policies from expert demonstrations. However, the capacity of the robot to practically record these demonstration results from real-world experiments can be challenging. Ref. [159] has introduced a method to acquire a mapping from actions to states using ego-centric videos collected by a human demonstrator using a mobile phone camera. The policy learning step was executed within a simulation platform; then, the developed navigation policy was tested on a Clearpath Jackal wheeled robot in an indoor environment. The robot and the trained videos had view-point mismatches, but the model was robust to those changes. The system was successfully able to map camera sensor inputs to actuator commands using the developed imitation learning policy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Advantages** & **Disadvantages** \\ \hline DQN & Updates are done offline, are not complex, and are reliable & Only discrete motions \\ \hline DDPG & High sample efficiency, less data correlation and faster convergence compared to DQN & The poor generalisation of novel environments \\ \hline TRPO & Ensure stable convergence & Too many assumptions, may create large errors \\ \hline PPO & Simplified solution process, good performance and easier to implement compared to TRPO & Low sampling efficiency \\ \hline A3C & Asynchronous parallel network training, fast convergence, suitable for multi-robot systems & Require large training data, difficult to migrate model to real world \\ \hline SAC & Better robustness and sample efficiency compared to the above methods & Bulky model size \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DRL motion planning methods. Inverse RL is another approach capable of learning reward functions from expert demonstrations. However, inverse RL is more appropriate for exploring novel environments because imitation learning tries to directly follow a demonstrator rather than improve beyond that knowledge level. The creation of an inverse RL network by employing a vision-based imitation learning method was presented by [160]. This method approximates a value function from one middle layer of a policy trained by imitation learning. The related experiments were carried out on a real-world setup and using the ROS Gazebo simulation platform. This proposed method has shown the capability of generalising the system for unseen environments by producing usable cost maps. Traditional geometric-based SLAM procedures lack the capacity to capture dynamic objects in external environments and are thus only suitable for planning robot actions in static environments. RL-based techniques, however, can learn policies by considering dynamic obstacles in outdoor environments, although the sample efficiency is lower compared to imitation learning and model-based learning. The implementation of learning-based maps and traditional path planners is one way to improve sample efficiency. A mobile robot affordance map generation using the RL policy-based method was introduced by [161]. This research used metric cost maps (attaching semantics and geometry) for robot navigation. The A* classical path planner was implemented for path generation. This learning-based SLAM approach was implemented using simulation software. Semantic, dynamic, and behavioural attributes of unseen environments have been learnt by the model using the simulation scheme.