Chapter 4 - Hard

Policy-Based Reinforcement Learning

• Policy-Based Reinforcement Learning: A method that directly optimizes the policy that the agent follows, without explicitly using a value function.

Core Problem

• Core Problem in Policy-Based RL: Finding the optimal policy in environments with continuous action spaces, such as robotics, self-driving cars, and real-time strategy games.

Core Algorithms

• Policy Gradient Methods: Optimize the policy by following the gradient of expected reward with respect to the policy parameters.
• Examples include REINFORCE and Actor-Critic methods.

Jumping Robots

• Example: Jumping robots in continuous action spaces illustrate the need for policy-based methods to handle the infinite possibilities of action values.

Continuous Problems

• Continuous Problems: These are problems where the action space is continuous rather than discrete.
• Examples include robotic control and real-time strategy games where actions can take any value within a range.

Continuous Policies

• Definition: Policies that can take on any value within a range, suitable for environments where actions are not discrete but continuous.
• Equation: The policy π(a|s, θ) represents the probability of taking action a in state s parameterized by θ.

Stochastic Policies

• Definition: Policies that introduce randomness in action selection to encourage exploration and prevent premature convergence to suboptimal actions.
• Equation: The stochastic policy is often modeled by a probability distribution, e.g., π(a|s, θ) = N (µ(s, θ), σ(s, θ)) for a Gaussian distribution.

Environments: Gym and MuJoCo

• Gym: A toolkit for developing and comparing reinforcement learning algorithms with a wide variety of environments.
• MuJoCo: A physics engine for simulating continuous control tasks, often used in reinforcement learning research for robotics.

Applications

• Robotics: Using policy-based methods to control robotic arms, drones, and other mechanical systems with continuous actions.
• Physics Models: Simulating realistic physical interactions in environments to train agents for tasks like walking, jumping, or manipulating objects.
• Games: Training agents in complex games with continuous actions, such as strategy games and simulations.

Policy-Based Agents

• Policy-Based Agents: Agents that use policy gradient methods to optimize their actions.
• These agents directly learn the policy that maps states to actions.

REINFORCE Algorithm

• Algorithm: Update policy parameters θ ← θ + α∇θ log π(a|s, θ)R, where R is the return.

Online and Batch

• Online: Updating the policy parameters after every episode.
• Batch: Accumulating gradients over multiple episodes and then updating the parameters.

Bias-Variance Trade-Off in Policy-Based Methods

• Explanation: Balancing the trade-off between bias (error due to approximations) and variance (error due to randomness) to ensure stable and efficient learning.

Actor-Critic Bootstrapping

• Algorithm: Update actor parameters θ and critic parameters ϕ using temporal difference bootstrapping.

Temporal Difference Bootstrapping

• Definition: A method that updates value estimates based on estimates of future values.
• Equation: The update rule for the state-value function V is: V (st) ← V (st) + α[rt+1 + γV (st+1) − V (st)].

Baseline Subtraction with Advantage Function

• Advantage Function: Measures how much better an action is compared to the average action in a given state.
• Equation: The deterministic policy gradient is ∇θ J(θ) = E[∇a Q(s, a)∇θ µ(s, θ)].

PPO and DDPG MuJoCo Examples

• PPO: Proximal Policy Optimization, a method that simplifies TRPO while retaining performance.
• DDPG: Applying DDPG to control tasks in MuJoCo to demonstrate policy learning in continuous action spaces.

Locomotion and Visuo-Motor Environments

• Locomotion and Visuo-Motor Environments: These environments focus on training agents to move and interact with their surroundings using visual inputs and motor actions.

Locomotion

• Application: Training agents to move and navigate in environments, such as walking, running, or flying.
• Example: Using policy-based methods to train a bipedal robot to walk or a drone to fly through an obstacle course.

Visuo-Motor Interaction

• Application: Combining visual perception with motor control to interact with objects and environments.
• Example: Training an agent to play table tennis by integrating visual input to track the ball and motor control to hit it accurately.

Benchmarking

• Explanation: Using standardized tasks and environments to evaluate and compare the performance of reinforcement learning algorithms.
• Example: Evaluating different policy-based methods on common benchmarks like MuJoCo locomotion tasks or Atari games to compare their effectiveness.