Risk-Sensitive Reinforcement Learning: A Comparative Analysis

Summary

  • 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

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