rainbow#

Source code: tianshou/algorithm/modelfree/rainbow.py

class RainbowTrainingStats(*, loss: float)[source]#

Bases: object

loss: float#
class RainbowDQN(*, policy: C51Policy, optim: OptimizerFactory, gamma: float = 0.99, n_step_return_horizon: int = 1, target_update_freq: int = 0)[source]#

Bases: C51

Implementation of Rainbow DQN. arXiv:1710.02298.

Parameters:

  • policy – a policy following the rules (s -> action_values_BA)

  • optim – the optimizer factory for the policy’s model.

  • gamma – the discount factor in [0, 1] for future rewards. This determines how much future rewards are valued compared to immediate ones. Lower values (closer to 0) make the agent focus on immediate rewards, creating “myopic” behavior. Higher values (closer to 1) make the agent value long-term rewards more, potentially improving performance in tasks where delayed rewards are important but increasing training variance by incorporating more environmental stochasticity. Typically set between 0.9 and 0.99 for most reinforcement learning tasks

  • n_step_return_horizon – the number of future steps (> 0) to consider when computing temporal difference (TD) targets. Controls the balance between TD learning and Monte Carlo methods: higher values reduce bias (by relying less on potentially inaccurate value estimates) but increase variance (by incorporating more environmental stochasticity and reducing the averaging effect). A value of 1 corresponds to standard TD learning with immediate bootstrapping, while very large values approach Monte Carlo-like estimation that uses complete episode returns.

  • target_update_freq – the number of training iterations between each complete update of the target network. Controls how frequently the target Q-network parameters are updated with the current Q-network values. A value of 0 disables the target network entirely, using only a single network for both action selection and bootstrap targets. Higher values provide more stable learning targets but slow down the propagation of new value estimates. Lower positive values allow faster learning but may lead to instability due to rapidly changing targets. Typically set between 100-10000 for DQN variants, with exact values depending on environment complexity.