Dueling Network Architectures for Deep Reinforcement Learning

Resources

• Paper

• Introduction
  • Complementary approach - can be used with pre-existing algorithms
  • Dueling Architecture
    • Separates state-value and state-dependent action advantages into two streams (one network) → ultimately still outputs a Q-value function
    • Shares a common convolution module
  • Dueling architecture learns which states are valuable without having to learn effect of action for each state
    • Good when actions don’t impact the environment in relevant way
• Background
  • Advantage function: $A^\pi(s,a) = Q^\pi(s,a) - V^\pi(s)$
    • Expectation of advantage is 0
    • Obtains the relative importance of each action
  • Deep Q Networks
    • Use a separate target network that gets updates every n iterations
    • Replay buffer for data efficiency
  • Double Deep Q Networks
    • DQN faces over-optimism bias because of max operator to select and evaluate action
    • DDQN target is: $y_i^{DDQN} = r + \gamma Q(s’, argmax_{a’}Q(s’, a’; \theta_i); \theta^-)$
  • Prioritized Replay
    • Prioritized Replay increases replay probability based on experience with high expected learning progress (proxy of absolute TD error)
    • Leads to faster learning and better quality final policies
• The Dueling Network Architecture
  • Key insight: in many states, unnecessary to estimate value of action choice whereas in others its paramount to know which action to take
  • In bootstrapping, estimation of state values is important for every state
  • Two streams, one for advantage and one for value function
    • Combined to produce estimate of Q-value
  • Can be used in many classical RL algorithms like SARSA or DDQN
  • Design
    • One stream outputs a scalar: $V(s; \theta, \beta)$
    • Other stream outputs a $\vert A\vert$-dimensional vector: $A(s, a, \theta, \alpha)$
      • $\theta$: parameters of convolutional layers
      • $\beta$: parameters of value function layers
      • $\alpha$: parameters of advantage function layers
    • We cannot use definition of advantage to compute Q-value: $Q(s, a; \theta, \alpha,\beta) = V(s;\theta,\beta) + A(s, a; \theta\alpha)$
      • Q is a paramterized estimate of true Q value function
      • V might not be a good estimator for state-value
      • A might not be a good estimator for advantage
    • Q value function is unidentifiable: Cannot decompose A and V from Q
      • We can force the advantage estimator to have 0 advantage at chosen action → last module of network implements: $Q(s, a; \theta, \alpha,\beta) = V(s;\theta,\beta) + A(s, a; \theta\alpha) - max_{a’ \in \vert A\vert}A(s, a’;\theta, \alpha)$
      • By doing this, we can make sure that Q = V when the optimal action is chosen (via identifability)
      • Alternatively, you can subtract the average advantage → losing semantics of Q and V but stabalizes optimization
        • Advantages only need to change as fast as mean
        • Does not change the relative rank of A → preserves greedy policy based on Q-values
• Experiments
  • Policy Evaluation
    • Use temporal difference learning with expected SARSA but don’t modify behavior policy
    • Epsilon greedy policy
    • Architecture
      • 3 layer MLP with 50 hidden units
      • Each stream with 2 layers with 25 hidden units
    • With 5 actions, both architectures converge at same speed
    • With more actions, dueling does better
      • V stream learns general value function shared across many similar actions at S → faster convergence
  • General Atari Game Playing
    • Architecture:
      • Shared: 3 convolutional layers, 2 fully connected layers
      • Streams: 1 fully connected layer with 512 units
        • Final layer for V: single unit output
        • Final layer for A: outputs as many units as actions possible
    • Training:
      • Rescale gradient by $\frac{1}{\sqrt(2)}$
      • Clip gradients to have norm less than 10
    • To test effects of dueling, train DDQN with clipped gradients
    • Dueling does better than clipped DDQN and regular DDQN
  • Robustness to Human Starts: Agent performs well simply remembering sequences of actions (because Atari is deterministic)
    • Even with human starts, duel outperforms clipped DDQN
  • Combined with prioritized experience replay
    • Gradient clipping and prioritized replay have adverse interactions (PER samples have high TD error)
      • Fixed with retuning of learning rate
  • Saliency Maps: Compute the magnitude of the jacobians of A and V with respect to the inputs
    • Shows where each stream of the network interacts with the input frames
• Discussion
  • Dueling architecture learns state-value function efficiently
    • Each Q value update results in a V value update
    • Better approximation of V → needed for TD methods
  • Differences in Q across states small relative to magnitude of Q
    • Small updates = cause reordering in actions → greedy policy switches abruptly
    • Dueling architecture with separate stream can counter these effects