Maximum Entropy Reinforcement Learning (Stochastic Control)Dongmin Lee
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I reviewed the following papers.
- T. Haarnoja, et al., Reinforcement Learning with Deep Energy-Based Policies", ICML 2017
- T. Haarnoja, et al., Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor", ICML 2018
- T. Haarnoja, et al., Soft Actor-Critic Algorithms and Applications", arXiv preprint 2018
Thank you.
Reinforcement Learning (RL) approaches to deal with finding an optimal reward based policy to act in an environment (Charla en Ingls)
However, what has led to their widespread use is its combination with deep neural networks (DNN) i.e., deep reinforcement learning (Deep RL). Recent successes on not only learning to play games but also superseding humans in it and academia-industry research collaborations like for manipulation of objects, locomotion skills, smart grids, etc. have surely demonstrated their case on a wide variety of challenging tasks.
With application spanning across games, robotics, dialogue, healthcare, marketing, energy and many more domains, Deep RL might just be the power that drives the next generation of Artificial Intelligence (AI) agents!
1. Policy gradient methods estimate the optimal policy through gradient ascent on the expected return. They directly learn stochastic policies without estimating value functions.
2. REINFORCE uses Monte Carlo returns to estimate the policy gradient. It updates the policy parameters in the direction of the gradient to maximize expected returns.
3. PPO improves upon REINFORCE by clipping the objective function to restrict how far the new policy can be from the old policy, which helps stabilize training. It uses a surrogate objective and importance sampling to train the policy on data collected from previous policies.
Reinforcement Learning with Deep Energy-Based PoliciesSangwoo Mo
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This document discusses reinforcement learning with deep energy-based policies. It motivates using maximum entropy reinforcement learning to find policies that not only maximize reward but also explore possibilities. It presents an approach using energy-based models for the policy and soft Q-learning to find the optimal maximum entropy policy. The method uses neural networks to approximate the soft Q-function and a sampling network to draw samples from the policy. Experiments show maximum entropy policies provide better exploration, initialization, compositionality and robustness compared to deterministic policies.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
Soft Actor-Critic is an off-policy maximum entropy deep reinforcement learning algorithm that uses a stochastic actor. It was presented in a 2017 NIPS paper by researchers from OpenAI, UC Berkeley, and DeepMind. Soft Actor-Critic extends the actor-critic framework by incorporating an entropy term into the reward function to encourage exploration. This allows the agent to learn stochastic policies that can operate effectively in environments with complex, sparse rewards. The algorithm was shown to learn robust policies on continuous control tasks using deep neural networks to approximate the policy and action-value functions.
Reinforcement Learning (RL) approaches to deal with finding an optimal reward based policy to act in an environment (Charla en Ingls)
However, what has led to their widespread use is its combination with deep neural networks (DNN) i.e., deep reinforcement learning (Deep RL). Recent successes on not only learning to play games but also superseding humans in it and academia-industry research collaborations like for manipulation of objects, locomotion skills, smart grids, etc. have surely demonstrated their case on a wide variety of challenging tasks.
With application spanning across games, robotics, dialogue, healthcare, marketing, energy and many more domains, Deep RL might just be the power that drives the next generation of Artificial Intelligence (AI) agents!
1. Policy gradient methods estimate the optimal policy through gradient ascent on the expected return. They directly learn stochastic policies without estimating value functions.
2. REINFORCE uses Monte Carlo returns to estimate the policy gradient. It updates the policy parameters in the direction of the gradient to maximize expected returns.
3. PPO improves upon REINFORCE by clipping the objective function to restrict how far the new policy can be from the old policy, which helps stabilize training. It uses a surrogate objective and importance sampling to train the policy on data collected from previous policies.
Reinforcement Learning with Deep Energy-Based PoliciesSangwoo Mo
?
This document discusses reinforcement learning with deep energy-based policies. It motivates using maximum entropy reinforcement learning to find policies that not only maximize reward but also explore possibilities. It presents an approach using energy-based models for the policy and soft Q-learning to find the optimal maximum entropy policy. The method uses neural networks to approximate the soft Q-function and a sampling network to draw samples from the policy. Experiments show maximum entropy policies provide better exploration, initialization, compositionality and robustness compared to deterministic policies.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
Soft Actor-Critic is an off-policy maximum entropy deep reinforcement learning algorithm that uses a stochastic actor. It was presented in a 2017 NIPS paper by researchers from OpenAI, UC Berkeley, and DeepMind. Soft Actor-Critic extends the actor-critic framework by incorporating an entropy term into the reward function to encourage exploration. This allows the agent to learn stochastic policies that can operate effectively in environments with complex, sparse rewards. The algorithm was shown to learn robust policies on continuous control tasks using deep neural networks to approximate the policy and action-value functions.
The guided policy search(GPS) is the branch of reinforcement learning developed for real-world robotics, and its utility is substantiated along many research. This slide show contains the comprehensive concept of GPS, and the detail way to implement, so it would be helpful for anyone who want to study this field.
DeepSeek??? ????? ??? Trend (Faculty Tae Young Lee)Tae Young Lee
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The document titled "Trends Observed Through DeepSeek" explores advancements in AI and reinforcement learning through the lens of DeepSeek's latest developments. It is structured into three main sections:
DeepSeek-V3
Focuses on context length extension, initially supporting 32,000 characters and later expanding to 128,000 characters.
Introduces Mixture of Experts (MoE) architecture, optimizing computational efficiency using a novel Auxiliary-Loss-Free Load Balancing strategy.
Multi-Head Latent Attention (MLA) reduces memory consumption while maintaining performance, enhancing large-scale model efficiency.
DeepSeek-R1-Zero
Explores advancements in reinforcement learning algorithms, transitioning from RLHF to GRPO (Group Relative Policy Optimization) for cost-effective optimization.
Direct Preference Optimization (DPO) enhances learning by leveraging preference-based optimization instead of traditional reward functions.
DeepSeek-R1 and Data Attribution
Discusses a Cold Start approach using high-quality data (SFT) to ensure stable initial training.
Incorporates reasoning-focused reinforcement learning, balancing logical accuracy with multilingual consistency.
Utilizes rejection sampling and data augmentation to refine AI-generated outputs for enhanced usability and safety.
The document provides a detailed analysis of these methodologies, positioning DeepSeek as a key player in AI model development and reinforcement learning.