Robotic RL Training Data from Engineered Policies for Sparse Rewards
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Solution Overview
Problem
Reinforcement learning (RL) for robotic tasks faces challenges in early stages of training due to minimal policy training, resulting in random actions and sparse rewards, especially in large or continuous state and action spaces, leading to ineffective training.
Innovation Solution
Utilizing manually engineered policies to generate RL data that includes state-action pairs and rewards, enabling successful task performance and efficient training by sampling actions within the action space, with optional exploration beyond the engineered policy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is applied to robotic tasks with minimal policy training, then the robot can explore the state and action spaces, but the robot takes random actions that rarely result in successful task performance
Solution Approach 1:
The system performs preliminary action by generating synthetic training data using a manually engineered policy before actual reinforcement learning training. This pre-generated data includes successful state-action-reward trajectories that bootstrap the RL agent, allowing it to start from a knowledgeable baseline rather than complete randomness, thus improving early-stage task success rates while maintaining exploration capability
2Adaptability or versatility
If the state space and action space are large and continuous for robotic tasks, then the robot can perform complex tasks, but the reinforcement learning training becomes resource-intensive and inefficient
Solution Approach 1:
The system performs preliminary action by pre-generating synthetic training data that covers the large and continuous state-action spaces using a manually engineered policy. This approach allows comprehensive coverage of complex task scenarios without requiring the RL agent to explore everything from scratch, significantly reducing the computational resources and time needed for training while maintaining the ability to handle complex robotic tasks
3Device complexity
If reinforcement learning reward is sparse and only provided at the end of an episode, then the reward function is simple to define, but the policy cannot be effectively trained due to lack of intermediate feedback
Solution Approach 1:
The system performs preliminary action by generating synthetic data that includes dense intermediate rewards and guidance signals throughout episode trajectories. This pre-computed guidance information is incorporated into the training dataset, providing the RL agent with frequent feedback about progress toward the goal without requiring complex reward function design, thus improving training effectiveness while keeping the reward function simple
Data Source
AI summary
Utilizing at least one existing policy (e.g. a manually engineered policy) for a robotic task, in generating reinforcement learning (RL) data that can be used in training an RL policy for an instance of RL of the robotic task. The existing policy can be one that, standing alone, will not generate data that is compatible with the instance of RL for the robotic task. In contrast, the generated RL data is compatible with RL for the robotic task at least by virtue of it including state data that is in a state space of the RL for the robotic task, and including actions that are in the action space of the RL for the robotic task. The generated RL data can be used in at least some of the initial training for the RL policy using reinforcement learning.