AI BriefingDecision Transformer Solves Reinforcement Learning as a Sequence Modeling Problem
Key point
Decision Transformer converts reinforcement learning problems into sequence modeling problems, achieving stable policy learning through the Transformer architecture.
Details
Traditional Deep RL utilizes Temporal Difference (TD) learning in the process of finding a policy that maximizes reward as an agent interacts with the environment. However, TD learning has the limitation that it is difficult to stack network layers deeply due to the instability of target values.
To address this problem, the proposed Decision Transformer (DT) redefines reinforcement learning as a sequence modeling problem from natural language processing. Instead of learning the existing action-value function, DT directly learns what action should be taken to achieve a desired value in a specific state.
**The main features of DT are as follows:
- Utilizes the Transformer architecture to stably stack complex layers
- Learns the policy via Supervised Learning, overcoming the instability of TD learning
- Optimized for Offline RL, which utilizes given data without real-time interaction with the environment
Recently, the Generalized DT model, which extends this DT architecture, has also been drawing attention.
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