Meta-Policy Exploration Control for Reinforcement Learning Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models face challenges in efficiently training deep neural networks, particularly in complex environments with large observation-action spaces, where exploration strategies are often suboptimal and require extensive hyperparameter tuning.
Innovation Solution
The system employs a meta policy to control an exploration strategy that modifies the base policy neural network's outputs, allowing for stochastic action selection and efficient data generation during training, thereby reducing the need for hyperparameter searches and improving sample efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional reinforcement learning exploration strategies are used, then the agent can interact with the environment, but the training requires extensive hyperparameter tuning and has suboptimal exploration efficiency
Solution Approach 1:
The meta-policy network automatically adjusts exploration parameters by observing the base policy's performance and self-generating appropriate exploration strategies, eliminating the need for manual hyperparameter tuning and making the system self-configuring
Solution Approach 2:
A meta-policy network is introduced as an intermediary between the base policy and the environment, which mediates the exploration process by dynamically adjusting exploration parameters based on observed performance, thereby optimizing training efficiency
2Speed
If the base policy neural network is updated frequently, then the convergence rate improves, but the training data generation becomes less efficient due to suboptimal exploration
Solution Approach 1:
The meta-policy network receives feedback about the base policy's performance through observed transitions and rewards, uses this feedback to adjust exploration parameters, and thereby optimizes the efficiency of training data generation while supporting frequent base policy updates
3Adaptability or versatility
If exploration strategy parameters are fixed, then the system is simpler to implement, but the exploration becomes suboptimal for different task domains
Solution Approach 1:
The exploration strategy transitions from static fixed parameters to dynamic parameter adjustment, where the meta-policy network continuously adapts exploration parameters based on real-time performance observations, enabling the system to adapt to different task domains
Solution Approach 2:
The system changes exploration parameters dynamically based on performance feedback rather than using fixed parameters, allowing the exploration strategy to adapt to different task characteristics and improve generalization across domains
Data Source
AI summary
The invention describes the method performed by one or more computers and for training a base policy neural network that is configured to receive a base policy input comprising an observation of a state of an environment and to process the policy input to generate a base policy output that defines an action to be performed by an agent in response to the observation, the method comprising: generating training data for training the base policy neural network by controlling an agent using (i) the base policy neural network and (ii) an exploration strategy that maps, in accordance with a set of one or more parameters, base policy outputs generated by the base policy neural network to actions performed by the agent to interact with an environment, the generating comprising, at each of a plurality of time points: determining that criteria for updating the exploration strategy are satisfied at the time point; and in response to determining that the criteria are satisfied: generating a meta policy input that comprises data characterizing a performance of the base policy neural network in controlling the agent at the time point; processing the meta policy input using a meta policy to generate a meta policy output that specifies respective values for each of the set of one or more parameters that define the exploration strategy; and controlling the agent using the base policy neural network and in accordance with the exploration strategy defined by the respective values for the set of one or more parameters specified by the meta policy output.