  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.
