Statements (132)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:video_games
gptkb:environment |
| gptkbp:adapted_into |
Real-World Applications
Different Scenarios |
| gptkbp:agent |
gptkb:Multi-Agent_Systems
gptkb:Agent |
| gptkbp:analyzes |
heat maps
Heat Maps pathfinding algorithms grid representation Graphical Interface 3 D Models |
| gptkbp:can |
multiple agents
dynamic obstacles varying rewards |
| gptkbp:can_be_customized_with |
Users
|
| gptkbp:can_be_extended_by |
custom rules
new features Custom Rules additional agents |
| gptkbp:can_be_simulated_in |
Simulators
|
| gptkbp:can_be_used_for |
Algorithm Benchmarking
|
| gptkbp:can_contain |
Obstacles
|
| gptkbp:can_have_different_agent_types |
Agent Variants
|
| gptkbp:can_have_different_end_conditions |
End Game Conditions
|
| gptkbp:can_have_different_goals |
Variable Goals
|
| gptkbp:can_have_different_rules |
Custom Game Rules
|
| gptkbp:can_have_different_starting_points |
Starting Positions
|
| gptkbp:can_have_dynamic_elements |
Dynamic Obstacles
|
| gptkbp:can_have_multiple_levels |
Level Design
|
| gptkbp:can_have_multiple_outcomes |
Outcome Variability
|
| gptkbp:can_have_rewards |
Negative Rewards
Positive Rewards |
| gptkbp:can_include |
Time Constraints
Random Events different terrains User Challenges different goal states varied agent types Rewards and Penalties |
| gptkbp:difficulty_levels |
Difficulty Settings
|
| gptkbp:educational_use |
Basic AI Concepts
|
| gptkbp:emulation |
gptkb:MATLAB
gptkb:Java gptkb:Python |
| gptkbp:features |
grid layout
|
| gptkbp:has |
state space
action space |
| gptkbp:has_actions |
Move Down
Move Left Move Right Move Up |
| gptkbp:has_goal |
Goal State
|
| gptkbp:has_grid_size |
Variable
|
| https://www.w3.org/2000/01/rdf-schema#label |
Grid World
|
| gptkbp:includes |
gptkb:Agent
obstacles rewards |
| gptkbp:is_a |
Reinforcement Learning Environment
|
| gptkbp:is_a_solution_for |
gptkb:Algorithms
|
| gptkbp:is_analyzed_in |
Efficiency
Data Analysis Tools Learning Strategies Agent Behavior |
| gptkbp:is_considered_as |
a classic problem
a benchmark problem a testbed for algorithms |
| gptkbp:is_evaluated_by |
Performance Metrics
|
| gptkbp:is_explored_in |
gptkb:Workshops
Conferences Theses Academic Papers |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:Java gptkb:C++ gptkb:Python gptkb:R gptkb:Keras gptkb:Unity gptkb:gymnasium gptkb:Pygame |
| gptkbp:is_incorporated_in |
Feedback Loops
User Feedback User Interactions Learning Algorithms User-Defined Parameters |
| gptkbp:is_part_of |
gptkb:machine_learning
AI Development AI Research Projects AI Curriculum AI Simulations |
| gptkbp:is_popular_among |
gptkb:students
|
| gptkbp:is_popular_in |
gptkb:machine_learning
Educational Settings robotics research AI community machine learning courses |
| gptkbp:is_related_to |
Q-learning
Markov Decision Processes policy iteration value iteration Markov decision processes |
| gptkbp:is_represented_in |
gptkb:Matrix
|
| gptkbp:is_tested_for |
gptkb:researchers
Simulation Tools Benchmarks Various Algorithms Real-Time Strategies |
| gptkbp:is_used_for |
Pathfinding
Behavior Analysis algorithm testing AI training benchmarking algorithms Algorithm Testing simulation experiments |
| gptkbp:is_used_in |
gptkb:AI_Research
gptkb:academic_research Game Development educational purposes |
| gptkbp:is_utilized_in |
Simulation Studies
|
| gptkbp:layout |
Custom Grids
|
| gptkbp:modifications |
Parameters
User Input parameters grid size agent behavior reward structure |
| gptkbp:used_in |
reinforcement learning
|
| gptkbp:was_a_demonstration_of |
Decision Making
Reinforcement Learning Concepts |
| gptkbp:bfsParent |
gptkb:gymnasium
|
| gptkbp:bfsLayer |
4
|