Improved Memories Learning

Abstract

We propose Improved Memories Learning (IMeL), a novel algorithm that turns reinforcement learning (RL) into a supervised learning (SL) problem and delimits the role of neural networks (NN) to interpolation. IMeL consists of two components. The first is a reservoir of experiences. Each experience is updated based on a non-parametric procedural improvement of the policy, computed as a bounded one-sample Monte Carlo estimate. The second is a NN regressor, which receives as input improved experiences from the reservoir (context points) and computes the policy by interpolation. The NN learns to measure the similarity between states in order to compute long-term forecasts by averaging experiences, rather than by encoding the problem structure in the NN parameters. We present preliminary results and propose IMeL as a baseline method for assessing the merits of more complex models and inductive biases.

References in corpus (7)

• Deep Reinforcement Learning that Matters
• When to use parametric models in reinforcement learning?
• A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
• A Closer Look at Deep Policy Gradients
• Training Agents using Upside-Down Reinforcement Learning
• Remember and Forget for Experience Replay
• Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions