[PDF] Exploring Deep Reinforcement Learning with Multi Q|Learning

(PDF) Exploring Deep Reinforcement Learning with Multi Q-Learning

To ensure the convergence of value function, a discount factor is involved in the value function. The temporal difference method is introduced to training the Q ...

Exploring Deep Reinforcement Learning with Multi Q-Learning

Q-learning is a popular temporal-difference reinforcement learning algorithm which often explicitly stores state values using lookup tables. This implementation ...

[PDF] Exploring Deep Reinforcement Learning with Multi Q-Learning

This paper presents a new algorithm called Multi Q- learning to attempt to overcome the instability seen in Q-learning, ...

Exploring Deep Reinforcement Learning with Multi Q-Learning

We test our algorithm on a 4 × 4 grid-world with different stochastic reward functions using various deep neural networks and convolutional.

Deep Reinforcement Learning with Double Q-Learning

exploration technique (Kaelbling et al. 1996). If, however, the ... A deep Q network (DQN) is a multi-layered neural network that for a given ...

A further exploration of deep Multi-Agent Reinforcement Learning ...

The research of extending deep reinforcement learning (drl) to multi-agent field has solved many complicated problems and made great ...

[PDF] Deep Reinforcement Learning with Double Q-Learning

Exploring Deep Reinforcement Learning with Multi Q-Learning · E. DuryeaMichael GangerWei Hu. Computer Science. 2016. TLDR. This paper presents a new algorithm ...

Exploration in Deep Reinforcement Learning: A Survey - arXiv

While updating Q-learning, the next actions at+1 are sampled from the behaviour policy which follows an -greedy exploration strategy, and among ...

Exploring Multi-View Perspectives on Deep Reinforcement Learning ...

We trained CNN based Deep Q-learning embodied agents with egocentric, allocen- tric, and combined egocentric-allocentric perspectives to locate an object in an ...

(PDF) An exploration of reinforcement learning and deep ...

PDF | Today, machine learning is evolving so quickly that new algorithms are always appearing. Deep neural networks in particular have shown ...

Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

1, CMAE achieves similar or better performance than the baselines in dense-reward settings. We also compare the exploration behavior of CMAE to Q- learning with ...

Cooperative Exploration for Multi-Agent Deep Reinforcement Learning

The Q-table is initial- ized to zero. The update step size for exploration policies and target policies are 0.1 and 0.05 respectively. For Island we use a DQN ( ...

Multi-Agent Deep Reinforcement Learning - CS231n

We also use a residual neural network as the Q-value func- tion approximator. The approach is shown to generalize multi-agent policies to new environments, and ...

Efficient Multi-step Exploration for Deep Reinforcement Learning

In practice, we learn Bayesian posterior over Q-function in discrete cases and over action in continuous cases to approximate uncertainty in each step and ...

Deep Reinforcement Learning Variants of Multi-Agent Learning ...

Two novel variants of Deep Q-Network (DQN). These algorithms are designed with the intention of providing architectures that are more appropriate for handling ...

Exploration in Deep Reinforcement Learning: From Single-Agent to ...

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL) have achieved significant success across a wide range of ...

Exploration with Exemplar Models for Deep Reinforcement Learning

For example, bootstrapped DQN. (Osband et al., 2016) avoids the need to construct a generative model of the state by instead training multiple, randomized value ...

Deep multiagent reinforcement learning: challenges and directions

Since rewards may be delayed, an agent has to make a trade-off between exploiting states with the current highest reward and exploring states ...

Robust Multi-Agent Reinforcement Learning via Minimax Deep ...

(2016) extended the deep Q-learning to multi- agent setting; Peng et al ... The idea of one-step-gradient approximation was also explored in meta-.

Multi-Scale Deep Reinforcement Learning for Real-Time 3D ...

Q-Learning algorithm [53], a deep Q-network can be trained in a RL setup using an iterative approach to minimize the mean squared error based on the Bellman ...