No abstract

To ensure the steady navigation for robot stable controls are one of the basic requirements. Control values selection is highly environment dependent. To ensure reusability of control parameter system needs to generalize over the environment. Adding adaptability in robots to perform effectively in the environments with no prior knowledge reinforcement leaning is a promising approach. However, tuning hyper parameters and attaining correlation between state space and reward function to train a stable reinforcement learning agent is a challenge. In this paper we designed a continuous reward function to minimizing the sparsity and stabilizes the policy convergence, to attain control generalization for differential drive robot. We Implemented Twin Delayed Deep Deterministic Policy Gradient on Open-AI Gym Race Car. System was trained to achieve smart primitive control policy, moving forward in the direction of goal by maintaining an appropriate distance from walls to avoid collisions. Resulting policy was tested on unseen environments including dynamic goal environment, boundary free environment and continuous path environment on which it outperformed Deep Deterministic Policy Gradient.

To ensure the steady navigation for robot stable controls are one of the basic requirements. Control values selection is highly environment dependent. To ensure reusability of control parameter system needs to generalize over the environment. Adding adaptability in robots to perform effectively in the environments with no prior knowledge reinforcement leaning is a promising approach. However, tuning hyper parameters and attaining correlation between state space and reward function to train a stable reinforcement learning agent is a challenge. In this paper we designed a continuous reward function to minimizing the sparsity and stabilizes the policy convergence, to attain control generalization for differential drive robot. We Implemented Twin Delayed Deep Deterministic Policy Gradient on Open-AI Gym Race Car. System was trained to achieve smart primitive control policy, moving forward in the direction of goal by maintaining an appropriate distance from walls to avoid collisions. Resulting policy was tested on unseen environments including dynamic goal environment, boundary free environment and continuous path environment on which it outperformed Deep Deterministic Policy Gradient.