parameters: (updated_strategy, gamma)
return_statement: return updated_strategy
docstring: Projects the distribution in updated_x to have minimal probabilities.
  
    Minimal probabilities are set as gamma, and the probabilities are then
    renormalized to sum to 1.
  
    Args:
      updated_strategy: New distribution value after being updated by update rule.
      gamma: minimal probability value when divided by number of actions.
  
    Returns:
      Projected distribution.
docstring_summary: Projects the distribution in updated_x to have minimal probabilities.
docstring_tokens: Projects the distribution in updated_x to have minimal probabilities .
function: def _project_distribution(updated_strategy, gamma):
    """Projects the distribution in updated_x to have minimal probabilities.
  
    Minimal probabilities are set as gamma, and the probabilities are then
    renormalized to sum to 1.
  
    Args:
      updated_strategy: New distribution value after being updated by update rule.
      gamma: minimal probability value when divided by number of actions.
  
    Returns:
      Projected distribution.
    """
    # Epsilon approximation of L2-norm projection onto the Delta_gamma space.
    updated_strategy[updated_strategy < gamma] = gamma
    updated_strategy = updated_strategy / np.sum(updated_strategy)
    return updated_strategy
function_tokens: def _project_distribution ( updated_strategy , gamma ) : # Epsilon approximation of L2-norm projection onto the Delta_gamma space. updated_strategy [ updated_strategy < gamma ] = gamma updated_strategy = updated_strategy / np . sum ( updated_strategy ) return updated_strategy