RL Planning Component With Coarse-State Value Propagation
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Solution Overview
Problem
Current reinforcement learning methods for training agents, such as robots, face challenges in achieving high success rates and consistent performance across diverse environments, particularly with varying terrain types, due to limitations in handling high-dimensional observations and unknown dynamics.
Innovation Solution
The method involves a planning component that divides the environment into coarse-scale states, derives traversability information, assigns values based on traversability and rewards, and trains using an auxiliary loss to improve the agent's decision-making process, incorporating an actor and critic network to optimize actions and value predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is used to train agents for navigation tasks, then the agent can learn to perform desired behaviors, but the agent achieves low success rates and high performance variance in diverse environments with different terrain types
Solution Approach 1:
The environment is divided into discrete map representations and coarse-scale states, breaking down the continuous navigation problem into manageable discrete units that the planning component can process effectively
Solution Approach 2:
A planning component is introduced as an intermediary between the reinforcement learning agent and the environment. This planning component uses discrete map representations and value propagation to guide the agent's actions, improving reliability while the RL component handles high-dimensional observations
2Productivity
If the environment is divided into coarse-scale states according to a grid, then the planning component can efficiently assign values to states, but the resolution of environmental representation is reduced
Solution Approach 1:
The environment is segmented into coarse-scale grid states for planning purposes, enabling efficient value propagation and computation while maintaining the ability to represent detailed environments through the discrete map structure
Solution Approach 2:
The problem transitions from continuous spatial representation to discrete grid-based representation, adding a computational dimension that enables efficient value iteration and planning while the underlying continuous environment details are preserved in the map representation
3Extent of automation
If an actor-critic control strategy is added to the planning part, then the entire architecture can be trained through back-propagation, but the device complexity increases
Solution Approach 1:
The planning component and actor-critic control strategy are merged into a unified trainable architecture. The planning component's value maps serve as input to the actor-critic, and gradients from the actor-critic losses are back-propagated through the planning part, enabling joint training of the entire system
Data Source
AI summary
A method for training an agent having a planning component. The method includes carrying out a plurality of control passes, and training the planning component to reduce a loss that includes, for each of a plurality of coarse-scale state transitions occurring in the control passes from a coarse-scale state to a coarse-scale successor state, an auxiliary loss that represents a deviation between a value outputted by the planning component for the coarse-scale state and the sum of a reward received for the coarse-scale state transition and at least a portion of the value of the coarse-scale successor state.