# Facilitating Robot Learning in Virtual Environments: A Deep Reinforcement Learning Framework
## 2 Related Work  
Reinforcement learning [6] is a branch of machine learning focused on enabling agents to learn optimal behaviors by interacting with their environment through trial and error. RL leverages a system of rewards and penalties to encourage learning through feedback, allowing agents to make decisions that maximize cumulative rewards [7]. A cornerstone of this methodology is the Markov decision process (MDP), which formalizes the interaction between an agent and its environment in terms of states, actions, transition probabilities, and rewards [8]. A key feature of RL is the balance between exploration and exploitation. Exploration refers to testing new actions to discover potentially better strategies, while exploitation focuses on leveraging known strategies to maximize immediate rewards. Modern RL employs advanced algorithms like proximal policy optimization (PPO) [9], deep Q-networks (DQN), and advantage actor-critic (A2C) [10], which allow agents to tackle complex, high-dimensional tasks effectively. The OpenAI Gym (now Gymnasium) framework has emerged as a vital tool in the development and testing of RL algorithms [11]. It provides a standardized interface for a diverse range of simulated environments, from simple tasks like CartPole to intricate robotic control simulations [2]. Gym supports both discrete and continuous action spaces, enabling researchers and practitioners to benchmark their RL models under consistent conditions. By offering ready-to-use environments, it accelerates experimentation, promotes reproducibility, and fosters innovation in the RL community. The Gym framework's modularity extends its usability, allowing developers to create custom environments tailored to specific applications. For instance, robotic RL, with tools like Gym's robotics suite, which integrate simulated tasks with physics engines like MuJoCo [12], enables training of manipulation or navigation policies in virtual settings before deploying them in real-world scenarios. This ability to simulate environments mitigates risks and reduces costs during the training phase, making Gym a pivotal resource for RL research. Deep reinforcement learning (DRL) [13], which combines RL with deep neural networks, has further enhanced the scope of Gym applications. Frameworks like Stable-Baselines3 [14] and [PERSON]'s RLlib [15] build on Gym to provide robust RL algorithm implementations, simplifying the process of training agents in Gym-compatible environments. These integrations underscore Gym's flexibility and its role in advancing state-of-the-art RL techniques. In summary, RL principles, bolstered by frameworks like OpenAI Gym, continue to drive progress in artificial intelligence and robotics. By providing accessible, standardized environments, Gym empowers researchers and practitioners to push the boundaries of what autonomous agents can achieve in simulated and real-world scenarios. As reinforcement learning (RL) problems increase in complexity, simulation environments become essential for development and testing. Simulation reduces the need for direct experimentation with physical systems, which is particularly critical for tasks with high degrees of freedom, such as autonomous driving. Consequently, simulation platforms like Webots [16] are increasingly important, especially those that are open-source. Alternatives to Webots include Gazebo [17], RoboDK [18], CoppeliaSim, OpenRave, and Unity [19], all of which support the integration of deep reinforcement learning (DRL) algorithms. This paper focuses on Webots due to its capability to create robots from scratch, its realistic graphics, and its compatibility with the Robot Operating System (ROS). However, many aspects of the proposed framework can be adapted to other simulation environments. These simulation platforms typically provide pre-built robot models, ranging from simple robots like the E-puck [20] and iCub [21] to industrial robots such as the UR5e [22] and IRB 4600 [23], which are utilized in this research.