Reinforcement Learning Agent Planning via Learned Dynamics Models
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Solution Overview
Problem
Reinforcement learning systems face challenges in planning actions for agents in complex environments with unknown dynamics, especially when a perfect simulator is not available, leading to inefficiencies and high computational overhead.
Innovation Solution
A reinforcement learning system uses a learned dynamics model and prediction model to predict next environment states and action scores, allowing for tree-based planning without reconstructing full environment states, and employs action sampling to reduce the action space, enabling efficient action selection in complex domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reinforcement learning systems use a simulator to perform planning iterations, then they can accurately determine environment state transitions, but they suffer from high computational overhead and inefficiency when perfect simulators are not available
Solution Approach 1:
The patent creates a learned dynamics model that copies the essential transition dynamics of the environment without requiring a perfect simulator. This model is trained offline to replicate state transitions, enabling planning iterations to proceed efficiently using the learned model instead of costly simulator calls or real-world interactions.
Solution Approach 2:
The system performs preliminary training of the dynamics model and value function offline before actual planning and execution. This preliminary action prepares the system in advance, so that during runtime planning iterations, the system can quickly query pre-learned transition dynamics and value estimates without performing expensive simulations or real-world trials.
2Adaptability or versatility
If the system performs tree-based planning by traversing the state tree with all possible actions, then it can explore the full action space, but it incurs excessive computational resources and latency
Solution Approach 1:
The patent applies partial action by sampling only a subset of actions at each planning iteration rather than exhaustively evaluating all possible actions. The value function guides this sampling to focus on promising actions, achieving sufficient exploration without the computational burden of complete action space traversal.
Solution Approach 2:
The system changes the parameter of action space granularity by working with sampled action subsets instead of the full action space. The value function outputs provide probabilistic guidance that effectively prunes the action space, transforming an intractable computation into a manageable one while preserving exploration of meaningful actions.
3Loss of information
If the system reconstructs full environment states for planning, then it can maintain complete state information, but it increases computational overhead and memory requirements
Solution Approach 1:
The patent extracts only the essential elements needed for planning by using the learned dynamics model to predict next states directly from current states and actions. Instead of reconstructing and maintaining full environment state representations, the system extracts transition probabilities and value estimates, which are sufficient for planning decisions without the overhead of complete state reconstruction.
4Measurement precision
If the system uses real-world interactions for training, then it learns accurate environment dynamics, but it causes wear and tear on physical agents and requires extensive time and resources
Solution Approach 1:
The system performs preliminary offline training of the dynamics model using collected experience data before deployment. This preliminary action allows the model to learn accurate transition dynamics in advance, so that during actual agent operation, no additional real-world interactions are needed for planning, thereby eliminating ongoing wear and tear and reducing total training time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting actions to be performed by an agent interacting with an environment to cause the agent to perform a task. One of the methods includes: receiving a current observation characterizing a current environment state of the environment; performing a plurality of planning iterations to generate plan data that indicates a respective value to performing the task of the agent performing each of the set of actions in the environment and starting from the current environment state, wherein performing each planning iteration comprises selecting a sequence of actions to be performed by the agent starting from the current environment state based on outputs generated by a dynamics model and a prediction model; and selecting, from the set of actions, an action to be performed by the agent in response to the current observation based on the plan data.