Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models

1. 1Identify the potential causes of failure in Dyna agents
2. 2Learn accurate models of environment dynamics using multistep predecessor models
3. 3Update the value function with simulated experience generated by the environment model
4. 4Evaluate the performance of the Dyna agent with the mitigated value hallucination

Researchers and engineers working on reinforcement learning and Dyna-style planning can benefit from this approach to improve the accuracy of their models and agents

💡 Using multistep predecessor models can help reduce the impact of model errors on Dyna agents

💡 Mitigate value hallucination in Dyna planning with multistep predecessor models