OpenAI Gym

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

OpenAI Gym is a software toolkit that provides standardized environments and interfaces for developing and evaluating reinforcement-learning agents.

OpenAI Gym provides reinforcement-learning environments that expose observations, actions, rewards, resets, and episode termination. CartPole turns these interfaces into a concrete control problem: keep a pole upright while maximizing cumulative reward. section 12.3

CartPole with reinforcement-learning libraries

A2C or PPO can train an MLP policy on CartPole-v1 for 10,000 timesteps, then evaluate the policy over multiple episodes and render its behavior.

Figure 12.8 CartPole balancing problem

Use the CartPole figure alongside the implementation listings to connect state-action interaction with the training loop. section 12.3

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes

Demonstrates environment interaction and complete training scripts, but defers detailed environment design and algorithm derivations.

Examples

Uses CartPole-v1, actor-critic networks, episode-reward plots, evaluation episodes, and rendered agent behavior.

What it assumes

Assumes basic Python and reinforcement-learning terminology while demonstrating environment interaction from concrete code.

What you can do after

Operate/configure Gymnasium environments and train or evaluate CartPole agents with A2C or PPO libraries.

How it connects

Connects reinforcement learning with CartPole, A2C, PPO, PyTorch, Gymnasium, and Stable-Baselines3.

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Also explained in

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  Evolutionary Deep Learning: Genetic algorithms and neural networksGym is an open-source toolkit that provides standardized environments and interfaces for developing and evaluating reinforcement-learning algorithms.
• 

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  Sutskever's ListA software platform providing environments and interfaces for developing and evaluating reinforcement-learning agents.

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Related concepts

• reinforcement learninguses · Reinforcement learning is a machine-learning paradigm in which an agent learns a policy through interaction with an environment and evaluative feedback about its actions or outcomes.
• environmenthas part · An environment is the external context, including its entities and conditions, in which a system, process, or agent exists or operates.
• frozen lake problemuses · The frozen lake problem is a grid-navigation task in which an agent must reach a goal while avoiding hazardous holes.
• neuroevolution of augmenting topologiesused by · NeuroEvolution of Augmenting Topologies (NEAT) is an evolutionary method that evolves neural-network weights and connection topologies, typically through genomes and speciation.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.