TD Learning Update Rule

TD Learning

• TD Learning updates the value of a state based on the value of the next state and the reward received: V(s) ← V(s) + α[r + γV(s′) - V(s)]
• Example: An agent navigating a grid world updates the value of the current state using the estimated value of the next state.

Bias-Variance Trade-off

• Definition: Bias-variance trade-off balances the error introduced by bias and variance in machine learning.
• Bias: The error introduced by approximating a real-world problem with a simplified model.
• Variance: The error introduced by the model’s sensitivity to small fluctuations in the training set.
• Trade-off:
  • High bias (simplistic models): may not capture the underlying pattern (underfitting).
  • High variance (complex models): may capture noise in the training data as if it were a true pattern (overfitting).

Correlation and Convergence

• Correlation: Consecutive states are often correlated, leading to inefficient learning and convergence issues.
• Techniques like experience replay help decorrelate the training data.
• Convergence: Ensuring that the learning algorithm converges to an optimal policy.

Deadly Triad

• The combination of function approximation, bootstrapping, and off-policy learning can lead to instability and divergence in reinforcement learning algorithms.

Stable Deep Value-Based Learning

• Techniques to achieve stable learning in deep value-based agents:
  • Decorrelating states using experience replay.
  • Infrequent updates of target weights.
  • Hands-on practice with examples like DQN and Breakout.

Improving Exploration

• Exploration is crucial in reinforcement learning to discover optimal policies.
• Methods to improve exploration:
  • Addressing the overestimation problem in Q-learning.

Actor-Critic Bootstrapping

• Algorithm: Combines actor-critic methods to train a robot to navigate through an environment by continuously updating both the policy and value functions based on observed rewards and states.
• Temporal Difference Bootstrapping: Updates value estimates based on estimates of future values, combining dynamic programming and Monte Carlo methods.

Baseline Subtraction with Advantage Function

• Advantage Function: Measures how much better an action is compared to the average action in a given state.
• Equation: A(s, a) = Q(s, a) - V(s)
• Example: Improving policy updates by subtracting a baseline value from the observed rewards to reduce variance and make learning more stable.

Model-Based Methods

• Weakness: Model inaccuracies, especially in complex or high-dimensional environments, can lead to suboptimal planning and decision-making.
• Improvements:
  • Using ensemble models to capture uncertainty and reduce the impact of model inaccuracies.
  • Integrating model-free methods to refine policies based on real experiences.

MuZero

• Biggest drawback: High computational complexity and resource requirements.
• Wonderful aspect: Learning both the model and the policy end-to-end without prior knowledge of the environment’s dynamics.

Challenges

• Partial Observability: Agents have incomplete information about the environment or other agents, making it difficult to make optimal decisions.
• Nonstationary Environments: The environment changes over time, which can alter the strategies and behaviors that are effective.
• Large State Space: The complexity of the state space makes learning and planning computationally intensive and challenging.

Multi-Agent Reinforcement Learning Agents

• Competitive Behavior:
  • Counterfactual Regret Minimization (CFR): An algorithm for decision-making in games that minimizes regret by considering counterfactual scenarios.
  • Deep Counterfactual Regret Minimization (Deep CFR): A variant of CFR that uses deep learning to handle large state and action spaces.
• Cooperative Behavior:
  • Centralized Training/Decentralized Execution: Training agents together with shared information but allowing them to act independently during execution.