Скачать или смотреть Deep Reinforcement Learning to train locomotion activity in Robots

Deep reinforcement learning (DRL) is at the cutting edge right now and has finally reached a point where we can apply it in the real world. In this project, we use policy gradient methods that exist in reinforcement learning to achieve the state of the art performance in continuous control tasks. Our objective was to use intensive reinforcement learning to allow robots to learn locomotive gates. We simulated these continuous control functions in the Box2D and MuJoCo (Multi-Joint Dynamics with Contact) environment, which allowed us to see the results of the algorithms that we have used on agents for the task. The policy gradient algorithms we use are DDPG, SAC, and TD3. The algorithm is used on agents in environments that were derived from the JIM library. The two agents we worked with are half cheetahs and bipedal walkers. After training the model using the algorithm, we tuned the hyperparameters for optimal performance by the agent. It also involves generating graphs based on the performance of the agent which allows us to evaluate the reward from time to time trained. The results of our project are: - 1) we have successfully taught the agent to learn locomotive gates, 2) we optimized its performance by hyperparameter tuning and performance analysis, 3) graphs representing the agent's performance.

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locomotion-skill-in-robotics-9130865c3f06


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