Robot RL Option Policies for Cross-Task Knowledge Transfer
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Solution Overview
Problem
Reinforcement learning (RL) in robotics faces challenges such as the curse of dimensionality, high costs due to real-world interactions, and difficulties in specifying reward functions, leading to inefficient training processes and limited generalization when learning new tasks.
Innovation Solution
A method where a robot reuses knowledge learned from one task to efficiently solve another novel task within the same action and state space by extracting feature rewards and learning option policies, minimizing redundant and correlated features to maximize cumulative rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is applied to learn new tasks from scratch, then the robot can solve the task, but the training cost and time increase exponentially due to the curse of dimensionality
Solution Approach 1:
The system performs preliminary action by learning option policies from historical task data before encountering new tasks. The replay buffer stores transitions from previous tasks, and option policies are pre-learned from this historical data, allowing the robot to start with prior knowledge rather than from scratch when facing new tasks.
Solution Approach 2:
The system changes parameters by transforming the action space from primitive actions to option policies. Instead of learning policies that output primitive actions directly, the system learns option policies that output higher-level actions, effectively changing the parameter space and reducing the complexity of learning for new tasks.
2Reliability
If reinforcement learning interacts with the real world to learn tasks, then the robot gains practical skills, but the cost of experiments increases due to maintenance, damage risk, and human involvement
Solution Approach 1:
The system uses copying by creating a simulated environment that replicates the real-world task dynamics. Instead of interacting directly with the physical robot for all learning, the system collects transitions from real-world interactions and uses them to train in simulation, copying the essential dynamics without the associated costs of physical experimentation.
Solution Approach 2:
The system performs preliminary action by collecting transition data from real-world interactions during normal operation and storing it in a replay buffer. This preliminary data collection allows subsequent learning to occur in simulation or with minimal real-world interaction, reducing the cost of experiments while still capturing practical skill requirements.
3Ease of operation
If reinforcement learning uses explicitly defined reward functions, then the desired behavior can be specified, but the complexity of reward engineering increases and scaling becomes difficult
Solution Approach 1:
The system extracts features from states using a feature network, separating the complex reward specification into feature extraction and simple feature-based rewards. Instead of engineering complex reward functions over full states, the system extracts relevant features and provides simple rewards based on feature values, reducing reward engineering complexity.
Solution Approach 2:
The system changes parameters by transforming complex state representations into simplified feature representations. The feature network converts high-dimensional states into lower-dimensional feature vectors, and rewards are specified in terms of these features rather than full states, reducing the complexity of behavior specification.
4Ease of operation
If imitation learning is used to learn skills from collected data, then the robot can mimic demonstrated behaviors, but the system suffers from overfitting that impedes generalization to new tasks
Solution Approach 1:
The system segments the learning process into option policy learning and task-specific policy learning. Option policies are learned from historical data and represent generalizable skills, while task-specific policies are learned by selecting and combining these option policies. This segmentation prevents overfitting by separating general skill acquisition from task-specific adaptation.
Solution Approach 2:
The system introduces dynamics by making the policy selection adaptive rather than static. The policy selection network dynamically chooses which option policies to execute based on the current state and task requirements, allowing the system to generalize to new tasks by adapting its use of learned skills rather than being fixed to specific demonstrations.
Data Source
AI summary
A robot that includes an RL agent that is configured to learn a policy to maximize the cumulative reward of a task, to determine one or more features that are minimally correlated with each other. The features are then used as pseudo-rewards, called feature rewards, where each feature reward corresponds to an option policy, or skill, the RL agent learns to maximize. In an example, the RL agent is configured to select the most relevant features to learn respective option policies from. The RL agent is configured to, for each of the selected features, learn the respective option policy that maximizes the respective feature reward. Using the learned option policies, the RL agent is configured to learn a new (second) policy for a new (second) task that can choose from any of the learned option policies or actions available to the RL agent.