| As a core research direction in the field of artificial intelligence,reinforcement learning mainly addresses sequential decision-making problems and has been widely applied in areas such as autonomous driving,news recommendation,and robot control.However,due to challenges like the exploration-exploitation dilemma and high-dimensional state-action spaces,reinforcement learning faces the issue of low sample efficiency,which means that an intelligent agent requires a large amount of data from environment interactions to train high-performance models.In this thesis,we propose three methods to improve sample efficiency.First,we present a priority state initialization reinforcement learning algorithm aimed at reducing state repetition to enhance the exploration-exploitation efficiency.This algorithm selects states with higher rewards or state values from the experience replay buffer as the initial state for the next trajectory when each training round ends.Experimental results show that,compared to random state initialization,this method achieves higher sample efficiency and faster model convergence when using the TD3 algorithm in multiple MuJoCo environments.Second,to address the issue of overly conservative offline reinforcement learning policies,we propose a two-stage offline-online reinforcement learning algorithm.In the first stage,a conservative policy is used for offline training to reduce the demand for environment interactions.In the second stage,the conservative policy is gradually weakened to increase the exploration ability of online learning training,thereby further alleviating the exploration-exploitation trade-off.Experimental results demonstrate that this method outperforms conservative policies and direct use of online reinforcement learning algorithms in terms of sample efficiency when using the TD3 algorithm in multiple MuJoCo environments.Lastly,to explore unknown environments more extensively during data collection without affecting training speed,we introduce a model-free online-offline hybrid policy algorithm.This algorithm consists of multiple online reinforcement learning models with the same network structure but different parameters and one offline reinforcement learning model.The online part generates diverse data and updates policies by interacting with the environment using multiple isomorphic models with different parameters,while the offline part receives data from the online part for training and updates both online and offline policies.In this thesis,we conduct experiments using the DQN algorithm in multiple Atari game environments,and the results show that the sample efficiency of this algorithm surpasses that of general reinforcement learning algorithms.In conclusion,this thesis proposes three methods to improve reinforcement learning sample efficiency from various perspectives.Experimental results indicate that these methods achieve significant progress in enhancing sample efficiency,laying a foundation for further optimization of reinforcement learning algorithms to address challenges in real-world applications. |
