bandits

The Bandit Environments present an array of n-armed bandit scenarios tailored for reinforcement learning research, built upon the OpenAI Gym framework. Each environment is designed with unique probability and reward distributions, enabling users to delve into various payout mechanisms and strategies. In these environments, actions correspond to different bandits, with distinct probabilities assigned to the likelihood of receiving a reward. Users can easily access these distributions to assess their algorithms’ performance against known payout values, facilitating a deeper understanding of how different approaches perform in diverse scenarios.

The environments encompass a range of configurations, from deterministic setups where one action guarantees a payout, to stochastic options that incorporate randomness in both probabilities and rewards. Some configurations feature multiple bandits, each with randomly assigned probabilities, providing an unpredictable challenge for reinforcement learning agents. Others implement varying reward schemes, such as uniform distributions, where payouts are consistently drawn from the same range, or Gaussian distributions that introduce variability based on statistical principles. This collection of environments serves as a valuable testing ground for developing, evaluating, and benchmarking a wide range of reinforcement learning strategies.