On Convergence of Average|Reward Off|Policy Control Algorithms in...

How can I ensure convergence of DDQN, if the true Q-values for ...

I have full control over the MDP, including the reward function, which in my case is sparse (0 until the terminal episode). The rewards are ...

Exploring Deep Reinforcement Learning with Multi Q-Learning

Figure 4 shows the average return curve of all three algorithms. When the standard deviation of the reward function was 7 and the policy was exclusively ...

Policy Mirror Descent for Regularized Reinforcement Learning

In addition, this linear convergence feature is provably stable in the face of inexact policy evaluation and imperfect policy updates. Numerical experiments are ...

On-Policy v/s Off-Policy Learning | by Abhishek Suran

This probability can be split into two parts i.e probability of taking action 'At' in some state 'St' and the probability of ending up in some state 'St+1' by ...

MDP & Reinforcement Learning - Convergence Comparison of VI, PI ...

I have implemented VI (Value Iteration), PI (Policy Iteration), and QLearning algorithms using python. ... QLearning algorithm results with Reward ...

On the Convergence of Natural Policy Gradient and Mirror Descent ...

However, the result cannot be directly used to obtain a corresponding convergence result for average-reward MDPs by letting the discount factor tend to one. In ...

Convergence Rates of Average-Reward Multi-agent Reinforcement ...

To solve (5), agents must cooperate in their policy search. With each agent only exercising control over their localized policy, the globally optimal joint ...

Average-Reward Off-Policy Policy Evaluation with ... - NASA ADS

To address the deadly triad, we propose two novel algorithms, reproducing the celebrated success of Gradient TD algorithms in the average-reward setting. In ...

Convergence of Q-Learning with Linear Function Approximation

The optimal control process can be obtained from the optimal policy δ∗, which can in turn be obtained from Q∗. Therefore, the optimal control problem is solved ...

Convergence and Iteration Complexity of Policy Gradient Method for ...

for any ✓1, ✓2, and ✓, respectively. In the literature on policy gradient/actor-critic algorithms. [23]–[27], the boundedness of the reward function in ...

RL — Reinforcement Learning Algorithms Comparison - Jonathan Hui

On-policy methods are usually simpler, less variance, and faster to converge compared with off-policy. In the example above, we introduce ...

RL Course by David Silver - Lecture 7: Policy Gradient Methods

Reinforcement Learning Course by David Silver# Lecture 7: Policy Gradient Methods (updated video thanks to: John Assael) #Slides and more ...

Reinforcement learning - GeeksforGeeks

How Reinforcement Learning Works · Policy: A strategy used by the agent to determine the next action based on the current state. · Reward Function ...

Key Papers in Deep RL — Spinning Up documentation

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Haarnoja et al, 2018. Algorithm: SAC. c. Deterministic Policy ...

Policy Gradient Methods for Reinforcement Learning with Function ...

Such discontinuous changes have been identified as a key obstacle to estab- lishing convergence assurances for algorithms following the value-function approach.

Analyzing the convergence of Q-learning through Markov Decision ...

In policy improvement we try to find a better policy by choosing actions which maximize the reward given the value function V π. Policy ...

What is the difference between Q-learning and SARSA?

... policy μ, so it's an off-policy algorithm. In contrast, SARSA uses π all the time, hence it is an on-policy algorithm. More detailed ...

How important is it to understand proofs of convergence of RL ...

For infinite horizon control problem over finite state stationary MDP, the policy iteration algorithm gives a sequence of stationary policies, ...

Model-based Average Reward Reinforcement Learning

This algorithm was proved to converge to the gain-optimal policy for ergodic MDPs. Since most domains that we are interested in are non-ergodic, ...

"Greedy in the Limit with Infinite Exploration" convergence guarantee

In the limit (as t → ∞), the learning policy is greedy with respect to the learned Q-function (with probability 1). This makes a lot of sense to ...