Exploring Reinforcement Learning Techniques For Discrete And Continuous Control Tasks In The Mujoco Environment

Abstract

We leverage the fast physics simulator, MuJoCo to run tasks in a continuous control environment and reveal details like the observation space, action space, rewards, etc. for each task. We benchmark value-based methods for continuous control by comparing Q-learning and SARSA through a discretization approach, and using them as baselines, progressively moving into one of the state-of-the-art deep policy gradient method DDPG. Over a large number of episodes, Qlearning outscored SARSA, but DDPG outperformed both in a small number of episodes. Lastly, we also fine-tuned the model hyper-parameters expecting to squeeze more performance but using lesser time and resources. We anticipated that the new design for DDPG would vastly improve performance, yet after only a few episodes, we were able to achieve decent average rewards. We expect to improve the performance provided adequate time and computational resources.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyrahul2023exploring

Related papers

• Attraction-repulsion Actor-critic For Continuous Control Reinforcement Learning (2019)0.00
• Knowledge Transfer In Multi-task Deep Reinforcement Learning For Continuous Control (2020)0.00
• Unified Policy Optimization For Continuous-action Reinforcement Learning In Non-stationary Tasks And Games (2022)2.26
• Guided Exploration In Reinforcement Learning Via Monte Carlo Critic Optimization (2022)0.00
• DDPG++: Striving For Simplicity In Continuous-control Off-policy Reinforcement Learning (2020)0.00
• Solving Continuous Control Via Q-learning (2022)0.00
• Can Reinforcement Learning For Continuous Control Generalize Across Physics Engines? (2020)0.00
• Simple Random Search Provides A Competitive Approach To Reinforcement Learning (2018)0.00