Deep Reinforcement Learning

Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes are uncertain.

Markov decision process is a 4-tuple ( S , A , P a , R a ) , where:

• S

  

  is a set of states called the state space. The state space may be discrete or continuous, like the set of real numbers.

• A

  

  is a set of actions called the action space (alternatively, A s

  

  is the set of actions available from state s

  

  ). As for state, this set may be discrete or continuous.

• P a ( s , s ′ )

  

  is, on an intuitive level, the probability that action a

  

  in state s

  

  at time t

  

  will lead to state s ′

  

  at time t + 1

  

  . In general, this probability transition is defined to satisfy Pr ( s t + 1 ∈ S ′ ∣ s t = s , a t = a ) = ∫ S ′ P a ( s , s ′ ) d s ′ ,

  

  for every S ′ ⊆ S

  

  measurable. In case the state space is discrete, the integral is intended with respect to the counting measure, so that the latter simplifies as P a ( s , s ′ ) = Pr ( s t + 1 = s ′ ∣ s t = s , a t = a )

  

  ; In case S ⊆ R d

  

  , the integral is usually intended with respect to the Lebesgue measure.

• R a ( s , s ′ )

  

  is the immediate reward (or expected immediate reward) received after transitioning from state s

  

  to state s ′

  

  , due to action a

  

  .

A policy function π is a (potentially probabilistic) mapping from state space ( S ) to action space ( A ).

In general, the policy p is a mapping from states to a probability distribution over actions

The goal of RL is to find an optimal policy 𝝅∗, that achieves the maximum expected return from all states

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods.

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g. every pixel rendered to the screen in a video game) and decide what actions to perform to optimize an objective (e.g. maximizing the game score). Deep reinforcement learning has been used for a diverse set of applications including but not limited to robotics, video games, natural language processing, computer vision,[1] education, transportation, finance and healthcare.

reward function

Deep Reinforcement Learning (DRL) can be effectively used in games in combination with simulations by leveraging the simulation environment to train agents in a controlled, repeatable, and scalable manner. Here's a step-by-step guide on how this combination works and can be utilized:

1. Simulated Environment Creation

• Objective: Develop a simulation that mimics the game's environment, including its rules, physics, and interactions.

• Tools: Use game engines (e.g., Unity, Unreal Engine), specialized simulation frameworks (e.g., OpenAI Gym, PyBullet), or custom-built environments.

• Benefits: Simulations allow for extensive experimentation without real-world constraints, such as time, cost, or safety.

2. DRL Agent Setup

• Policy Representation: The agent's policy, which dictates its actions, is typically represented by a neural network.

• Reward Function: Define a reward function that provides feedback on the agent's performance, guiding it towards desirable outcomes (e.g., winning the game, achieving high scores).

• Action Space: Specify the possible actions the agent can take (e.g., moving, attacking, defending).

• Observation Space: Define what the agent can observe from the environment (e.g., current state, enemy positions).

3. Training Process

• Exploration vs. Exploitation: Use DRL algorithms (e.g., DQN, PPO, A3C) to balance exploration (trying new strategies) and exploitation (using known strategies).

• Experience Replay: Store experiences (state, action, reward, next state) in a buffer to improve learning stability and efficiency by reusing past experiences.

• Simulation Runs: Conduct numerous simulation runs to train the agent across diverse scenarios, enabling it to learn from a wide variety of experiences.

4. Performance Monitoring and Adjustment

• Evaluation Metrics: Track key performance indicators (e.g., win rate, average reward) to assess learning progress.

• Hyperparameter Tuning: Adjust learning rate, discount factor, and other hyperparameters to optimize training efficiency.

• Curriculum Learning: Gradually increase the complexity of tasks or scenarios in the simulation to help the agent learn more effectively.

5. Transfer to Real Game Environment

• Testing: After training in the simulation, test the DRL agent in the actual game environment to ensure it performs well under real conditions.

• Fine-Tuning: Further fine-tune the agent using data from the real game environment to account for discrepancies between the simulation and the actual game.

6. Continuous Learning and Adaptation

• Online Learning: Enable the agent to continue learning and adapting during actual gameplay by collecting new data and refining its policy.

• Model Updates: Periodically update the agent's model with new experiences to maintain performance and adapt to changes in the game.

Applications in Games:

• NPC Behavior: Train non-player characters (NPCs) to exhibit realistic, adaptive behaviors.

• Game Testing: Use DRL agents to test game mechanics, uncover bugs, and evaluate game balance.

• Player Assistance: Develop AI that assists players by offering hints, solving puzzles, or controlling game elements.

• Dynamic Difficulty Adjustment: Adjust the game's difficulty dynamically based on the player's skill level to enhance engagement.

By combining DRL with simulations, game developers can create sophisticated, adaptive AI agents that enhance gameplay experience, automate testing, and innovate new game mechanics.

Exploration is any action that lets the agent discover new features about the environment, while exploitation is capitalizing on knowledge already gained. If the agent continues to exploit only past experiences, it is likely to get stuck in a suboptimal policy. On the other hand, if it continues to explore without exploiting, it might never find a good policy.

An agent must find the right balance between the two so that it can discover the optimal policy that yields the maximum rewards.

Model-based learning knows the rules of the game — like a chess player who plans moves ahead, using a mental map. Model-free is more about trial-and-error, learning from doing rather than planning.

State transitions probabilities and rewards are initially not known. It must experience each state and each transition at least once to know the rewards, and it must experience them multiple times if it is to have a reasonable estimate of the transition probabilities

The optimal policy must be inferred by trial-and-error interaction with the environment. The only learning signal the agent receives is the reward.

The observations of the agent depend on its actions and can contain strong temporal correlations.

Agents must deal with long-range time dependencies: often the consequences of an action only materialize after many transitions of the environment. This is known as the (temporal) credit assignment problem

Software agent makes observations and takes actions within an environment, and in return it receives rewards.

Its objective is to learn to act in a way that will maximize its expected rewards over time