Latent-Space Reinforcement Learning for Farsighted Action Selection
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Solution Overview
Problem
Training policy neural networks for reinforcement learning agents is computationally intensive due to high-dimensional sensor data and inaccurate estimation of future rewards, leading to short-sighted actions and reduced task completion success.
Innovation Solution
Training policy neural networks using latent representations to predict long-term effects, reducing computational requirements and improving action selection by generating simulated state transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If policy neural networks are trained using high-dimensional sensor data, then the agent can make accurate action selections, but the computational resources required increase substantially
Solution Approach 1:
The patent extracts only the essential features from high-dimensional sensor data by training the policy neural network on latent representations generated by a separate representation network. This extraction process removes redundant information while preserving the critical elements needed for action selection, thereby reducing computational resource consumption during training while maintaining action selection accuracy.
Solution Approach 2:
The patent introduces latent representations as an intermediary between the raw sensor data and the policy neural network. The representation network acts as a mediator that transforms high-dimensional sensor observations into compressed latent states, which then serve as input to the policy network. This intermediary layer reduces the dimensionality and computational burden while preserving the information necessary for accurate action selection.
2Reliability
If the agent interacts extensively with the real-world environment during training, then the policy neural network learns more accurate behaviors, but the risk of damage to the agent increases
Solution Approach 1:
The patent creates a virtual copy of the real-world environment through simulated environments that replicate the dynamics and reward structures of the target domain. The policy neural network is trained in these simulated environments rather than directly in the real world, allowing the agent to learn accurate behaviors through extensive interaction without risking physical damage to the actual agent or its surroundings.
3Productivity
If the policy neural network focuses on immediate rewards, then the training process is simpler, but the agent performs short-sighted actions that reduce total future rewards
Solution Approach 1:
The patent incorporates preliminary action by having the policy neural network predict not only immediate rewards but also future rewards and actions through rollout simulations. The network performs preliminary planning by generating sequences of future actions and estimating their cumulative rewards, allowing it to select actions that optimize long-term outcomes rather than merely immediate gains, while still maintaining training efficiency through efficient prediction algorithms.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network used to select an action to be performed by an agent interacting with an environment. In one aspect, a method includes: receiving a latent representation characterizing a current state of the environment; generating a trajectory of latent representations that starts with the received latent representation; for each latent representation in the trajectory: determining a predicted reward; and processing the state latent representation using a value neural network to generate a predicted state value; determining a corresponding target state value for each latent representation in the trajectory; determining, based on the target state values, an update to the current values of the policy neural network parameters; and determining an update to the current values of the value neural network parameters.