Risk-Sensitive Reinforcement Learning: A Comparative Analysis


  • Empirically analysed the existing methods for risk sensitive RL various spanning risk measures like variance bounds and probabilty of risk bounds; incorporating them in algorithms like Q learning, SARSA and their risk-sensitive variants
  • Bench-marking on a Gridworld with error states, introduced a new risk measure that maximizes distance from error states per step