Deterministic Policy Training from Logged Robot Interactions
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Solution Overview
Problem
Existing systems for training computer-controlled systems, such as robotic systems, require numerous environment interactions, are data-inefficient, and suffer from high variance in collected data due to noise in actions and sensor measurements, leading to slow convergence and potential sub-optimal policies.
Innovation Solution
Implementing a deterministic policy with iterative optimization of parameters using accumulated reward distributions based on previous interaction logs, allowing for faster parameter learning and reduced interactions, and employing importance sampling to incorporate past experiences effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning techniques with stochastic policies are used to train computer-controlled systems, then the systems can learn to perform tasks through environment interactions, but the training requires many environment interactions which is costly and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-collecting interaction data from the environment before actual training begins. This offline data collection allows the system to prepare training datasets in advance, reducing the need for extensive online environment interactions during the training phase. The interaction logs are stored and reused multiple times for training different policies, thereby saving time and computational resources.
Solution Approach 2:
The patent uses copying by creating interaction logs that record state-action-reward transitions from environment interactions. These logs are copied and reused multiple times for training different policies without requiring additional environment interactions. This allows the same environmental data to serve multiple training purposes, significantly reducing the total number of interactions needed.
2Adaptability or versatility
If stochastic policies with noise injection are used for exploration, then the system can discover advantageous actions, but the noise causes high variance in collected data which slows convergence
Solution Approach 1:
The patent extracts the noise component from the policy execution by using deterministic policies during data collection. Instead of injecting noise into actions, the system collects clean interaction data without stochastic disturbances. This separation allows exploration to be achieved through other means (such as initial random policy variations) while maintaining data stability for reliable gradient estimation and faster convergence.
Solution Approach 2:
The patent inverts the conventional approach by collecting interaction data using deterministic policies rather than stochastic ones. Traditionally, noise is added during policy execution to enable exploration, but this patent collects data without noise and uses the accumulated reward distribution to guide policy improvement, thereby achieving exploration through the learning process itself rather than through noisy execution.
3Reliability
If conventional reinforcement learning methods are used, then policies can be optimized with respect to a reward function, but the methods are data-inefficient and cannot make full use of data from previous interactions
Solution Approach 1:
The patent implements feedback by accumulating reward distributions from previous interaction logs and using this historical information to guide subsequent policy optimization. The system continuously incorporates lessons learned from past interactions into the training process, allowing each training iteration to benefit from all previously collected data. This feedback mechanism ensures that no information from previous interactions is wasted.
Solution Approach 2:
The patent merges multiple interaction logs from different sources and time periods into a unified training dataset. By combining data from various environment interactions and using accumulated reward distributions across multiple policies, the system creates a comprehensive training resource that maximizes the utility of all collected information. This merging approach allows the system to learn from the aggregate experience rather than treating each interaction independently.
Data Source
AI summary
A system is described for configuring another system, e.g., a robotics system. The other system interacts with an environment according to a deterministic policy by repeatedly obtaining, from a sensor, sensor data indicative of a state of the environment, determining a current action, and providing, to an actuator, actuator data causing the actuator to effect the current action in the environment. To configure the other system, the system optimizes a loss function based on an accumulated reward distribution with respect to a set of parameters of the policy. The accumulated reward distribution includes an action probability of an action of a previous interaction log being performed according to the current set of parameters. The action probability is approximated using a probability distribution defined by an action selected by the deterministic policy according to the current set of parameters.