. . Problem 2: Developing a Deep Reinforcement Learning Agent with Hierarchical Policy Networks Background: Hierarchical Reinforcement Learning (HRL) aims to decompose complex tasks into simpler subtasks, enabling more efficient learning and better generalization. Integrating hierarchical policy networks within a deep reinforcement learning (DRL) framework can significantly enhance an agent's ability to solve intricate environments. Task: Develop a deep reinforcement learning agent that utilizes hierarchical policy networks to solve a complex multi-stage environment (eg. a simulated robotic manipulation task with multiple steps). Your solution should address the following components: 1. Hierarchical Structure Design: Define the hierarchy of policies, including high-level (manager) and low-level (worker) policies. ■ Specify how high-level policies set subgoals or select among low-level policies. 2. State and Action Spaces: . Describe how the state and action spaces are structured at each hierarchical level. Explain any modifications or abstractions made to the state/action representations to facilitate hierarchy. 3. Learning Algorithms: . Choose appropriate DRL algorithms for both high-level and low-level policies (e.g., DDPG, PPO, SAC). Justify your choices based on the properties of the environment and the hierarchical structure. 4. Subgoal Representation and Selection: . Design a mechanism for representing and selecting subgoals within the high-level policy. Ensure that subgoals are meaningful and lead to progress in the environment. 5. Temporal Abstraction and Option Framework: Incorporate temporal abstraction by allowing low-level policies to execute over multiple time steps. Utilize the options framework or an alternative approach to manage the initiation and termination of options. 6. Training Strategy: ⚫ Develop a training strategy that coordinates the learning of high-level and low-level policies. ⚫ Address potential issues such as non-stationarity and credit assignment across hierarchical levels. 7. Evaluation: . Propose a set of evaluation tasks to assess the agent's performance, sample efficiency, and generalization capabilities. Compare your hierarchical agent against a non-hierarchical baseline. Deliverables: A comprehensive description of the hierarchical policy architecture, including diagrams. Detailed explanations of the learning algorithms and training procedures used. Implementation details, including pseudocode or code snippets for key components. Experimental results demonstrating the effectiveness of the hierarchical approach, along with analysis and discussion.
. . Problem 2: Developing a Deep Reinforcement Learning Agent with Hierarchical Policy Networks Background: Hierarchical Reinforcement Learning (HRL) aims to decompose complex tasks into simpler subtasks, enabling more efficient learning and better generalization. Integrating hierarchical policy networks within a deep reinforcement learning (DRL) framework can significantly enhance an agent's ability to solve intricate environments. Task: Develop a deep reinforcement learning agent that utilizes hierarchical policy networks to solve a complex multi-stage environment (eg. a simulated robotic manipulation task with multiple steps). Your solution should address the following components: 1. Hierarchical Structure Design: Define the hierarchy of policies, including high-level (manager) and low-level (worker) policies. ■ Specify how high-level policies set subgoals or select among low-level policies. 2. State and Action Spaces: . Describe how the state and action spaces are structured at each hierarchical level. Explain any modifications or abstractions made to the state/action representations to facilitate hierarchy. 3. Learning Algorithms: . Choose appropriate DRL algorithms for both high-level and low-level policies (e.g., DDPG, PPO, SAC). Justify your choices based on the properties of the environment and the hierarchical structure. 4. Subgoal Representation and Selection: . Design a mechanism for representing and selecting subgoals within the high-level policy. Ensure that subgoals are meaningful and lead to progress in the environment. 5. Temporal Abstraction and Option Framework: Incorporate temporal abstraction by allowing low-level policies to execute over multiple time steps. Utilize the options framework or an alternative approach to manage the initiation and termination of options. 6. Training Strategy: ⚫ Develop a training strategy that coordinates the learning of high-level and low-level policies. ⚫ Address potential issues such as non-stationarity and credit assignment across hierarchical levels. 7. Evaluation: . Propose a set of evaluation tasks to assess the agent's performance, sample efficiency, and generalization capabilities. Compare your hierarchical agent against a non-hierarchical baseline. Deliverables: A comprehensive description of the hierarchical policy architecture, including diagrams. Detailed explanations of the learning algorithms and training procedures used. Implementation details, including pseudocode or code snippets for key components. Experimental results demonstrating the effectiveness of the hierarchical approach, along with analysis and discussion.
Fundamentals of Information Systems
8th Edition
ISBN:9781305082168
Author:Ralph Stair, George Reynolds
Publisher:Ralph Stair, George Reynolds
Chapter7: Knowledge Management And Specialized Information Systems
Section: Chapter Questions
Problem 17SAT
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Transcribed Image Text:.
.
Problem 2: Developing a Deep Reinforcement Learning Agent with
Hierarchical Policy Networks
Background: Hierarchical Reinforcement Learning (HRL) aims to decompose complex tasks into
simpler subtasks, enabling more efficient learning and better generalization. Integrating hierarchical
policy networks within a deep reinforcement learning (DRL) framework can significantly enhance an
agent's ability to solve intricate environments.
Task: Develop a deep reinforcement learning agent that utilizes hierarchical policy networks to solve
a complex multi-stage environment (eg. a simulated robotic manipulation task with multiple steps).
Your solution should address the following components:
1. Hierarchical Structure Design:
Define the hierarchy of policies, including high-level (manager) and low-level (worker)
policies.
■ Specify how high-level policies set subgoals or select among low-level policies.
2. State and Action Spaces:
.
Describe how the state and action spaces are structured at each hierarchical level.
Explain any modifications or abstractions made to the state/action representations to
facilitate hierarchy.
3. Learning Algorithms:
.
Choose appropriate DRL algorithms for both high-level and low-level policies (e.g., DDPG,
PPO, SAC).
Justify your choices based on the properties of the environment and the hierarchical
structure.
4. Subgoal Representation and Selection:
.
Design a mechanism for representing and selecting subgoals within the high-level policy.
Ensure that subgoals are meaningful and lead to progress in the environment.
5. Temporal Abstraction and Option Framework:
Incorporate temporal abstraction by allowing low-level policies to execute over multiple
time steps.
Utilize the options framework or an alternative approach to manage the initiation and
termination of options.
6. Training Strategy:
⚫ Develop a training strategy that coordinates the learning of high-level and low-level
policies.
⚫ Address potential issues such as non-stationarity and credit assignment across hierarchical
levels.
7. Evaluation:
. Propose a set of evaluation tasks to assess the agent's performance, sample efficiency, and
generalization capabilities.
Compare your hierarchical agent against a non-hierarchical baseline.
Deliverables:
A comprehensive description of the hierarchical policy architecture, including diagrams.
Detailed explanations of the learning algorithms and training procedures used.
Implementation details, including pseudocode or code snippets for key components.
Experimental results demonstrating the effectiveness of the hierarchical approach, along with
analysis and discussion.
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