Reinforcement Learning with Discriminator Ensembles for Exploration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models struggle to effectively train action selection neural networks in environments without task rewards, leading to limited exploration and slower task performance due to reliance on single discriminator models that discourage exploration when encountering new environments.
Innovation Solution
Utilizing an ensemble of discriminator models to generate unsupervised rewards based on prediction disagreement, encouraging exploration and skill learning by conditioning the action selection neural network on latent variables, and combining these rewards with task rewards for enhanced training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single discriminator model is used to generate unsupervised rewards, then the training process becomes simpler and faster, but exploration is discouraged when encountering new environments leading to limited skill acquisition
Solution Approach 1:
The single discriminator model is segmented into an ensemble of multiple discriminator models. Each discriminator model processes observations independently and provides individual predictions. This segmentation allows the system to capture diverse perspectives on the same observation, preventing any single model from dominating the reward signal and encouraging broader exploration of the environment.
Solution Approach 2:
The system combines predictions from multiple discriminator models into a composite reward signal. By aggregating the outputs of multiple discriminators (e.g., through averaging or other combination methods), the system creates a more robust and nuanced reward signal that balances exploration encouragement with training efficiency, resolving the contradiction between simple fast training and effective exploration.
2Adaptability or versatility
If unsupervised rewards are used to encourage exploration, then the agent can discover new environments and skills, but the training requires more computational resources and time
Solution Approach 1:
The discriminator models are pre-trained on available task reward data before being used to generate unsupervised rewards. This preliminary action allows the discriminators to learn meaningful patterns from labeled data, so that when they generate unsupervised rewards during exploration, the agent receives high-quality signals that accelerate learning rather than requiring extensive trial-and-error exploration.
Solution Approach 2:
The ensemble of discriminator models acts as an intermediary between the agent and the environment. Instead of the agent directly interacting with sparse task rewards, the discriminators mediate by generating dense unsupervised reward signals that guide exploration. This intermediary layer translates environmental observations into informative reward signals that reduce the overall training time while maintaining effective exploration.
3Reliability
If task rewards are used for training, then the agent learns to accomplish specific goals, but the agent fails to explore new parts of the environment leading to limited skill acquisition
Solution Approach 1:
The system merges task rewards and unsupervised rewards into a combined training signal. The task rewards ensure reliable goal completion by providing clear directional guidance, while the unsupervised rewards from the discriminator ensemble encourage exploration of new environments. This combination allows the agent to simultaneously learn to accomplish specific goals and discover new skills, resolving the contradiction between task reliability and environmental adaptability.
Solution Approach 2:
The relative weighting of task rewards and unsupervised rewards can be dynamically adjusted during training. Early in training, unsupervised rewards may be weighted higher to encourage broad exploration and skill discovery. As training progresses and the agent masters fundamental skills, task rewards can be weighted higher to refine goal-directed behavior. This dynamic balancing allows the system to achieve both exploration and task completion effectively.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
This specification describes a method performed by one or more data processing apparatus that includes: sampling a latent from a set of possible latents, selecting actions to be performed by an agent to interact with an environment over a sequence of time steps using an action selection neural network that is conditioned on the sampled latent, determining a respective reward received for each time step in the sequence of time steps using an ensemble of discriminator models, and training the action selection neural network based on the rewards using a reinforcement learning technique. Each discriminator model can process an observation to generate a respective prediction output that predicts which latent the action selection neural network was conditioned on to cause the environment to enter the state characterized by the observation.