# Finite Horizon Stochastic Game

📗 Number of periods (H): .
📗 Number of states (|S|): .
📗 Number of actions (|A_1|, |A_2|, ...): .
📗 Range of reward: min , max
📗 Constrains: bounds, worse case, zero sum
Uniform transition Random Game
📗 Mean rewards (R):

📗 Transition probabilities (T):

📗 Initial state (Mu):
📗 Number of episodes (K):
Random Policy (Uniformly distributed actions)
📗 Policy (P0):

📗 Variance of reward (Gaussian):
Coverage, Simulate
📗 Simulated data (E0, based on H, S, A, R, T, Mu, P0):

# Estimated Game

Estimate
📗 Estimated mean rewards (R0, based on E0):

📗 Estimated transition probabilities (T0, based on E0):

📗 Estimated initial state (Mu0, based on E0):

# Poison Attack

Zero-Target, Random Target Policy (Uniform random deterministic actions)
📗 Target Policy (P1):

📗 Epsilon:
Quadratic, Dominant, Nash, Ignore Off-Path, Poison
📗 Poisoned data (E1, based on H, S, A, R0, T0, Mu0, P1):

📗 Total cost:
Dominant, Rationalizability, Markov Perfect, Nash, Feasibility, Mean Feasibility
📗 List of costs:

# Estimated Poisoned Game

Estimate
📗 Estimated mean rewards (R1, based on E1):

📗 Estimated transition probabilities (T1, based on E1):

📗 Estimated initial state (Mu1, based on E1):
Q Function
📗 Q function without attack (Q0, based on H, S, A, R0, T0, Mu0, P1):

📗 Q function after attack (Q1, based on H, S, A, R1, T1, Mu1, P1):

# Printing

📗 Experiment name:


Save Load
📗 State names:
📗 Action names:
Print

📗 Repeated:
📗 Variable H S n A b eps : from to
Repeat
📗 Attack costs: