# Mixed Monte Carlo — Reinforcement Learning Coach 0.12.0 documentation

# Mixed Monte Carlo

Actions space: Discrete

References: Count-Based Exploration with Neural Density Models

## Network Structure

## Algorithm Description

### Training the network

In MMC, targets are calculated as a mixture between Double DQN targets and full Monte Carlo samples (total discounted returns).

The DDQN targets are calculated in the same manner as in the DDQN agent:

\(y_t^{DDQN}=r(s_t,a_t )+\gamma Q(s_{t+1},argmax_a Q(s_{t+1},a))\)

The Monte Carlo targets are calculated by summing up the discounted rewards across the entire episode:

\(y_t^{MC}=\sum_{j=0}^T\gamma^j r(s_{t+j},a_{t+j} )\)

A mixing ratio $alpha$ is then used to get the final targets:

\(y_t=(1-\alpha)\cdot y_t^{DDQN}+\alpha \cdot y_t^{MC}\)

Finally, the online network is trained using the current states as inputs, and the calculated targets. Once in every few thousand steps, copy the weights from the online network to the target network.

class `rl_coach.agents.mmc_agent.``MixedMonteCarloAlgorithmParameters`
  Parameters

    monte_carlo_mixing_rate – (float) The mixing rate is used for setting the amount of monte carlo estimate (full return) that will be mixes into the single-step bootstrapped targets.