Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning
This addresses the need for adaptive temperature control in MaxEnt RL to improve training stability and robustness, though it appears incremental as it builds on existing methods like Soft Q-Learning.
The paper tackles the problem of constant temperature in Maximum Entropy Reinforcement Learning by proposing a state-based temperature scheduling method, showing promising results in toy and Atari 2600 domains.
Maximum Entropy Reinforcement Learning (MaxEnt RL) algorithms such as Soft Q-Learning (SQL) and Soft Actor-Critic trade off reward and policy entropy, which has the potential to improve training stability and robustness. Most MaxEnt RL methods, however, use a constant tradeoff coefficient (temperature), contrary to the intuition that the temperature should be high early in training to avoid overfitting to noisy value estimates and decrease later in training as we increasingly trust high value estimates to truly lead to good rewards. Moreover, our confidence in value estimates is state-dependent, increasing every time we use more evidence to update an estimate. In this paper, we present a simple state-based temperature scheduling approach, and instantiate it for SQL as Count-Based Soft Q-Learning (CBSQL). We evaluate our approach on a toy domain as well as in several Atari 2600 domains and show promising results.